Importing and analyzing external data using a virtual reality welding system

Description

TECHNICAL FIELD

Certain embodiments relate to virtual reality simulation. More particularly, certain embodiments relate to systems and methods for providing arc welding training in a simulated virtual reality environment or augmented reality environment using real-time weld puddle feedback, and for providing the importing and analyzing of external data in a virtual reality welding system.

BACKGROUND

Learning how to arc weld traditionally takes many hours of instruction, training, and practice. There are many different types of arc welding and arc welding processes that can be learned. Typically, welding is learned by a student using a real welding system and performing welding operations on real metal pieces. Such real-world training can tie up scarce welding resources and use up limited welding materials. Recently, however, the idea of training using welding simulations has become more popular. Some welding simulations are implemented via personal computers and/or on-line via the Internet. However, current known welding simulations tend to be limited in their training focus. For example, some welding simulations focus on training only for “muscle memory”, which simply trains a welding student how to hold and position a welding tool. Other welding simulations focus on showing visual and audio effects of the welding process, but only in a limited and often unrealistic manner which does not provide the student with the desired feedback that is highly representative of real world welding. It is this actual feedback that directs the student to make necessary adjustments to make a good weld. Welding is learned by watching the arc and/or puddle, not by muscle memory.

Further limitations and disadvantages of conventional, traditional, and proposed approaches will become apparent to one of skill in the art, through comparison of such approaches with embodiments of the present invention as set forth in the remainder of the present application with reference to the drawings.

SUMMARY

An arc welding simulation has been devised on a virtual reality welding system that provides simulation of a weld puddle in a virtual reality space having real-time molten metal fluidity characteristics and heat absorption and heat dissipation characteristics. Data may be imported into the virtual reality welding system and analyzed to characterize a student welder's progress and to provide training.

In accordance with an embodiment, a virtual reality welding system includes a programmable processor-based subsystem, a spatial tracker operatively connected to the programmable processor-based subsystem, at least one mock welding tool capable of being spatially tracked by the spatial tracker, and at least one display device operatively connected to the programmable processor-based subsystem. The system is capable of simulating, in virtual reality space, a weld puddle having real-time molten metal fluidity and heat dissipation characteristics. The system is further capable of displaying the simulated weld puddle on the display device to depict a real-world weld. Based upon the student performance, the system will display an evaluated weld that will either be acceptable or show a weld with defects. External data may be imported to the virtual reality welding system and analyzed to determine the quality of a weld generated by a student welder, or to model sections of a welded custom assembly for training.

One embodiment provides a method. The method includes importing a first data set of welding quality parameters, being representative of a quality of a weld generated by a student welder during a real-world welding activity corresponding to a defined welding process, into a virtual reality welding system. The method also includes comparing a second data set of welding quality parameters stored on the virtual reality simulator, being representative of a quality of a virtual weld generated by the student welder during a simulated welding activity corresponding to the defined welding process on the virtual reality welding system, to the first data set using a programmable processor-based subsystem of the virtual reality welding system. The method further includes generating a numerical comparison score in response to the comparing using the programmable processor-based subsystem of the virtual reality welding system.

One embodiment provides a method. The method includes importing a first data set of measured welding parameters, generated during a real-world welding activity corresponding to a defined welding process performed by an expert welder using a real-world welding machine, into a virtual reality welding system. The method also includes storing a second data set of simulated welding parameters, generated during a simulated welding activity corresponding to the defined welding process as performed by a student welder using the virtual reality welding system, on the virtual reality welding system. The method further includes calculating a plurality of student welding quality parameters by comparing the first data set to the second data set using a programmable processor-based subsystem of the virtual reality welding system.

One embodiment provides a method. The method includes storing a first data set of simulated welding parameters, generated during a first simulated welding activity corresponding to a defined welding process performed by an expert welder using a virtual reality welding system, on the virtual reality welding system. The method also includes storing a second data set of simulated welding parameters, generated during a second simulated welding activity corresponding to the defined welding process as performed by a student welder using the virtual reality welding system, on the virtual reality welding system. The method further includes calculating a plurality of student welding quality parameters by comparing the first data set to the second data set using a programmable processor-based subsystem of the virtual reality welding system.

One embodiment provides a method. The method includes importing a digital model representative of a welded custom assembly into a virtual reality welding system. The method also includes analyzing the digital model to segment the digital model into a plurality of sections using a programmable processor-based subsystem of the virtual reality welding system, wherein each section of the plurality of sections corresponds to a single weld joint type of the welded custom assembly. The method further includes matching each section of the plurality of sections to a virtual welding coupon of a plurality of virtual welding coupons modeled in the virtual reality welding system using the programmable processor-based subsystem of the virtual reality welding system.

These and other features of the claimed invention, as well as details of illustrated embodiments thereof, will be more fully understood from the following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a first example embodiment of a system block diagram of a system providing arc welding training in a real-time virtual reality environment;

FIG. 2 illustrates an example embodiment of a combined simulated welding console and observer display device (ODD) of the system of FIG. 1;

FIG. 3 illustrates an example embodiment of the observer display device (ODD) of FIG. 2;

FIG. 4 illustrates an example embodiment of a front portion of the simulated welding console of FIG. 2 showing a physical welding user interface (WUI);

FIG. 5 illustrates an example embodiment of a mock welding tool (MWT) of the system of FIG. 1;

FIG. 6 illustrates an example embodiment of a table/stand (T/S) of the system of FIG. 1;

FIG. 7A illustrates an example embodiment of a pipe welding coupon (WC) of the system of FIG. 1;

FIG. 7B illustrates the pipe WC of FIG. 7A mounted in an arm of the table/stand (TS) of FIG. 6;

FIG. 8 illustrates various elements of an example embodiment of the spatial tracker (ST) of FIG. 1;

FIG. 9A illustrates an example embodiment of a face-mounted display device (FMDD) of the system of FIG. 1;

FIG. 9B is an illustration of how the FMDD of FIG. 9A is secured on the head of a user;

FIG. 9C illustrates an example embodiment of the FMDD of FIG. 9A mounted within a welding helmet;

FIG. 10 illustrates an example embodiment of a subsystem block diagram of a programmable processor-based subsystem (PPS) of the system of FIG. 1;

FIG. 11 illustrates an example embodiment of a block diagram of a graphics processing unit (GPU) of the PPS of FIG. 10;

FIG. 12 illustrates an example embodiment of a functional block diagram of the system of FIG. 1;

FIG. 13 is a flow chart of an embodiment of a method of training using the virtual reality training system of FIG. 1;

FIGS. 14A-14B illustrate the concept of a welding pixel (wexel) displacement map, in accordance with an embodiment of the present invention;

FIG. 15 illustrates an example embodiment of a coupon space and a weld space of a flat welding coupon (WC) simulated in the system of FIG. 1;

FIG. 16 illustrates an example embodiment of a coupon space and a weld space of a corner (tee joint) welding coupon (WC) simulated in the system of FIG. 1;

FIG. 17 illustrates an example embodiment of a coupon space and a weld space of a pipe welding coupon (WC) simulated in the system of FIG. 1;

FIG. 18 illustrates an example embodiment of the pipe welding coupon (WC) of FIG. 17;

FIGS. 19A-19C illustrate an example embodiment of the concept of a dual-displacement puddle model of the system of FIG. 1;

FIG. 20 illustrates the concept of importing welding quality parameters into a virtual reality welding system from a real-world welding machine;

FIG. 21 is a flow chart of an embodiment of a method to compare a student welder's real-world welding activity to the student welder's virtual welding activity;

FIG. 22 is a flow chart of an embodiment of a method to compare a student welder's virtual welding activity to an expert welder's real-world welding activity;

FIG. 23 illustrates the concept of importing measured welding parameters into a virtual reality welding system from a real-world welding machine;

FIG. 24 is a flow chart of a first embodiment of a method of generating a plurality of student welding quality parameters and a numerical student score;

FIG. 25 is a flow chart of a second embodiment of a method of generating a plurality of student welding quality parameters and a numerical student score;

FIG. 26 illustrates the concept of importing a digital model representative of a welded custom assembly into a virtual reality welding system;

FIG. 27 illustrates the concept of matching sections of a digital model representative of a welded custom part to a plurality of welding coupons; and

FIG. 28 is a flowchart of an embodiment of a method to generate a virtual welding training program for a welded custom part.

DETAILED DESCRIPTION

An embodiment of the present invention comprises a virtual reality arc welding (VRAW) system comprising a programmable processor-based subsystem, a spatial tracker operatively connected to the programmable processor-based subsystem, at least one mock welding tool capable of being spatially tracked by the spatial tracker, and at least one display device operatively connected to the programmable processor-based subsystem. The system is capable of simulating, in a virtual reality space, a weld puddle having real-time molten metal fluidity and heat dissipation characteristics. The system is also capable of displaying the simulated weld puddle on the display device in real-time. The real-time molten metal fluidity and heat dissipation characteristics of the simulated weld puddle provide real-time visual feedback to a user of the mock welding tool when displayed, allowing the user to adjust or maintain a welding technique in real-time in response to the real-time visual feedback (i.e., helps the user learn to weld correctly). The displayed weld puddle is representative of a weld puddle that would be formed in the real-world based on the user's welding technique and the selected welding process and parameters. By viewing a puddle (e.g., shape, color, slag, size, stacked dimes), a user can modify his technique to make a good weld and determine the type of welding being done. The shape of the puddle is responsive to the movement of the gun or stick. As used herein, the term “real-time” means perceiving and experiencing in time in a simulated environment in the same way that a user would perceive and experience in a real-world welding scenario. Furthermore, the weld puddle is responsive to the effects of the physical environment including gravity, allowing a user to realistically practice welding in various positions including overhead welding and various pipe welding angles (e.g., 1G, 2G, 5G, 6G). As used herein, the term “virtual weldment” refers to a simulated welded part that exists in virtual reality space. For example, a simulated welding coupon that has been virtually welded as described herein is an example of a virtual weldment.

FIG. 1 illustrates an example embodiment of a system block diagram of a system 100 providing arc welding training in a real-time virtual reality environment. The system 100 includes a programmable processor-based subsystem (PPS) 110. The system 100 further includes a spatial tracker (ST) 120 operatively connected to the PPS 110. The system 100 also includes a physical welding user interface (WUI) 130 operatively connected to the PPS 110 and a face-mounted display device (FMDD) 140 operatively connected to the PPS 110 and the ST 120. The system 100 further includes an observer display device (ODD) 150 operatively connected to the PPS 110. The system 100 also includes at least one mock welding tool (MWT) 160 operatively connected to the ST 120 and the PPS 110. The system 100 further includes a table/stand (T/S) 170 and at least one welding coupon (WC) 180 capable of being attached to the T/S 170. In accordance with an alternative embodiment of the present invention, a mock gas bottle is provided (not shown) simulating a source of shielding gas and having an adjustable flow regulator.

FIG. 2 illustrates an example embodiment of a combined simulated welding console 135 (simulating a welding power source user interface) and observer display device (ODD) 150 of the system 100 of FIG. 1. The physical WUI 130 resides on a front portion of the console 135 and provides knobs, buttons, and a joystick for user selection of various modes and functions. The ODD 150 is attached to a top portion of the console 135. The MWT 160 rests in a holder attached to a side portion of the console 135. Internally, the console 135 holds the PPS 110 and a portion of the ST 120.

FIG. 3 illustrates an example embodiment of the observer display device (ODD) 150 of FIG. 2. In accordance with an embodiment of the present invention, the ODD 150 is a liquid crystal display (LCD) device. Other display devices are possible as well. For example, the ODD 150 may be a touchscreen display, in accordance with another embodiment of the present invention. The ODD 150 receives video (e.g., SVGA format) and display information from the PPS 110.

As shown in FIG. 3, the ODD 150 is capable of displaying a first user scene showing various welding parameters 151 including position, tip to work, weld angle, travel angle, and travel speed. These parameters may be selected and displayed in real time in graphical form and are used to teach proper welding technique. Furthermore, as shown in FIG. 3, the ODD 150 is capable of displaying simulated welding discontinuity states 152 including, for example, improper weld size, poor bead placement, concave bead, excessive convexity, undercut, porosity, incomplete fusion, slag inclusion, excess spatter, overfill, and burnthrough (melt through). Undercut is a groove melted into the base metal adjacent to the weld or weld root and left unfilled by weld metal. Undercut is often due to an incorrect angle of welding. Porosity is cavity type discontinuities formed by gas entrapment during solidification often caused by moving the arc too far away from the coupon.

Also, as shown in FIG. 3, the ODD 50 is capable of displaying user selections 153 including menu, actions, visual cues, new coupon, and end pass. These user selections are tied to user buttons on the console 135. As a user makes various selections via, for example, a touchscreen of the ODD 150 or via the physical WUI 130, the displayed characteristics can change to provide selected information and other options to the user. Furthermore, the ODD 150 may display a view seen by a welder wearing the FMDD 140 at the same angular view of the welder or at various different angles, for example, chosen by an instructor. The ODD 150 may be viewed by an instructor and/or students for various training purposes. For example, the view may be rotated around the finished weld allowing visual inspection by an instructor. In accordance with an alternate embodiment of the present invention, video from the system 100 may be sent to a remote location via, for example, the Internet for remote viewing and/or critiquing. Furthermore, audio may be provided, allowing real-time audio communication between a student and a remote instructor.

FIG. 4 illustrates an example embodiment of a front portion of the simulated welding console 135 of FIG. 2 showing a physical welding user interface (WUI) 130. The WUI 130 includes a set of buttons 131 corresponding to the user selections 153 displayed on the ODD 150. The buttons 131 are colored to correspond to the colors of the user selections 153 displayed on the ODD 150. When one of the buttons 131 is pressed, a signal is sent to the PPS 110 to activate the corresponding function. The WUI 130 also includes a joystick 132 capable of being used by a user to select various parameters and selections displayed on the ODD 150. The WUI 130 further includes a dial or knob 133 for adjusting wire feed speed/amps, and another dial or knob 134 for adjusting volts/trim. The WUI 130 also includes a dial or knob 136 for selecting an arc welding process. In accordance with an embodiment of the present invention, three arc welding processes are selectable including flux cored arc welding (FCAW) including gas-shielded and self-shielded processes; gas metal arc welding (GMAW) including short arc, axial spray, STT, and pulse; gas tungsten arc welding (GTAW); and shielded metal arc welding (SMAW) including E6010 and E7010 electrodes. The WUI 130 further includes a dial or knob 137 for selecting a welding polarity. In accordance with an embodiment of the present invention, three arc welding polarities are selectable including alternating current (AC), positive direct current (DC+), and negative direct current (DC−).

FIG. 5 illustrates an example embodiment of a mock welding tool (MWT) 160 of the system 100 of FIG. 1. The MWT 160 of FIG. 5 simulates a stick welding tool for plate and pipe welding and includes a holder 161 and a simulated stick electrode 162. A trigger on the MWD 160 is used to communicate a signal to the PPS 110 to activate a selected simulated welding process. The simulated stick electrode 162 includes a tactilely resistive tip 163 to simulate resistive feedback that occurs during, for example, a root pass welding procedure in real-world pipe welding or when welding a plate. If the user moves the simulated stick electrode 162 too far back out of the root, the user will be able to feel or sense the lower resistance, thereby deriving feedback for use in adjusting or maintaining the current welding process.

It is contemplated that the stick welding tool may incorporate an actuator, not shown, that withdraws the simulated stick electrode 162 during the virtual welding process. That is to say that as a user engages in virtual welding activity, the distance between holder 161 and the tip of the simulated stick electrode 162 is reduced to simulate consumption of the electrode. The consumption rate, i.e. withdrawal of the stick electrode 162, may be controlled by the PPS 110 and more specifically by coded instructions executed by the PPS 110. The simulated consumption rate may also depend on the user's technique. It is noteworthy to mention here that as the system 100 facilitates virtual welding with different types of electrodes, the consumption rate or reduction of the stick electrode 162 may change with the welding procedure used and/or setup of the system 100.

Other mock welding tools are possible as well, in accordance with other embodiments of the present invention, including a MWD that simulates a hand-held semi-automatic welding gun having a wire electrode fed through the gun, for example. Furthermore, in accordance with other certain embodiments of the present invention, a real welding tool could be used as the MWT 160 to better simulate the actual feel of the tool in the user's hands, even though, in the system 100, the tool would not be used to actually create a real arc. Also, a simulated grinding tool may be provided, for use in a simulated grinding mode of the simulator 100. Similarly, a simulated cutting tool may be provided, for use in a simulated cutting mode of the simulator 100. Furthermore, a simulated gas tungsten arc welding (GTAW) torch or filler material may be provided for use in the simulator 100.

FIG. 6 illustrates an example embodiment of a table/stand (T/S) 170 of the system 100 of FIG. 1. The T/S 170 includes an adjustable table 171, a stand or base 172, an adjustable arm 173, and a vertical post 174. The table 171, the stand 172, and the arm 173 are each attached to the vertical post 174. The table 171 and the arm 173 are each capable of being manually adjusted upward, downward, and rotationally with respect to the vertical post 174. The arm 173 is used to hold various welding coupons (e.g., welding coupon 175) and a user may rest his/her arm on the table 171 when training. The vertical post 174 is indexed with position information such that a user may know exactly where the arm 173 and the table 171 are vertically positioned on the post 171. This vertical position information may be entered into the system by a user using the WUI 130 and the ODD 150.

In accordance with an alternative embodiment of the present invention, the positions of the table 171 and the arm 173 may be automatically set by the PSS 110 via preprogrammed settings, or via the WUI 130 and/or the ODD 150 as commanded by a user. In such an alternative embodiment, the T/S 170 includes, for example, motors and/or servo-mechanisms, and signal commands from the PPS 110 activate the motors and/or servo-mechanisms. In accordance with a further alternative embodiment of the present invention, the positions of the table 171 and the arm 173 and the type of coupon are detected by the system 100. In this way, a user does not have to manually input the position information via the user interface. In such an alternative embodiment, the T/S 170 includes position and orientation detectors and sends signal commands to the PPS 110 to provide position and orientation information, and the WC 175 includes position detecting sensors (e.g., coiled sensors for detecting magnetic fields). A user is able to see a rendering of the T/S 170 adjust on the ODD 150 as the adjustment parameters are changed, in accordance with an embodiment of the present invention.

FIG. 7A illustrates an example embodiment of a pipe welding coupon (WC) 175 of the system 100 of FIG. 1. The WC 175 simulates two six inch diameter pipes 175′ and 175″ placed together to form a root 176 to be welded. The WC 175 includes a connection portion 177 at one end of the WC 175, allowing the WC 175 to be attached in a precise and repeatable manner to the arm 173. FIG. 7B illustrates the pipe WC 175 of FIG. 7A mounted on the arm 173 of the table/stand (TS) 170 of FIG. 6. The precise and repeatable manner in which the WC 175 is capable of being attached to the arm 173 allows for spatial calibration of the WC 175 to be performed only once at the factory. Then, in the field, as long as the system 100 is told the position of the arm 173, the system 100 is able to track the MWT 160 and the FMDD 140 with respect to the WC 175 in a virtual environment. A first portion of the arm 173, to which the WC 175 is attached, is capable of being tilted with respect to a second portion of the arm 173, as shown in FIG. 6. This allows the user to practice pipe welding with the pipe in any of several different orientations and angles.

FIG. 8 illustrates various elements of an example embodiment of the spatial tracker (ST) 120 of FIG. 1. The ST 120 is a magnetic tracker that is capable of operatively interfacing with the PPS 110 of the system 100. The ST 120 includes a magnetic source 121 and source cable, at least one sensor 122 and associated cable, host software on disk 123, a power source 124 and associated cable, USB and RS-232 cables 125, and a processor tracking unit 126. The magnetic source 121 is capable of being operatively connected to the processor tracking unit 126 via a cable. The sensor 122 is capable of being operatively connected to the processor tracking unit 126 via a cable. The power source 124 is capable of being operatively connected to the processor tracking unit 126 via a cable. The processor tracking unit 126 is cable of being operatively connected to the PPS 110 via a USB or RS-232 cable 125. The host software on disk 123 is capable of being loaded onto the PPS 110 and allows functional communication between the ST 120 and the PPS 110.

Referring to FIG. 6, the magnetic source 121 of the ST 120 is mounted on the first portion of the arm 173. The magnetic source 121 creates a magnetic field around the source 121, including the space encompassing the WC 175 attached to the arm 173, which establishes a 3D spatial frame of reference. The T/S 170 is largely non-metallic (non-ferric and non-conductive) so as not to distort the magnetic field created by the magnetic source 121. The sensor 122 includes three induction coils orthogonally aligned along three spatial directions. The induction coils of the sensor 122 each measure the strength of the magnetic field in each of the three directions and provide that information to the processor tracking unit 126. As a result, the system 100 is able to know where any portion of the WC 175 is with respect to the 3D spatial frame of reference established by the magnetic field when the WC 175 is mounted on the arm 173. The sensor 122 may be attached to the MWT 160 or to the FMDD 140, allowing the MWT 160 or the FMDD 140 to be tracked by the ST 120 with respect to the 3D spatial frame of reference in both space and orientation. When two sensors 122 are provided and operatively connected to the processor tracking unit 126, both the MWT 160 and the FMDD 140 may be tracked. In this manner, the system 100 is capable of creating a virtual WC, a virtual MWT, and a virtual T/S in virtual reality space and displaying the virtual WC, the virtual MWT, and the virtual T/S on the FMDD 140 and/or the ODD 150 as the MWT 160 and the FMDD 140 are tracked with respect to the 3D spatial frame of reference.

In accordance with an alternative embodiment of the present invention, the sensor(s) 122 may wirelessly interface to the processor tracking unit 126, and the processor tracking unit 126 may wirelessly interface to the PPS 110. In accordance with other alternative embodiments of the present invention, other types of spatial trackers 120 may be used in the system 100 including, for example, an accelerometer/gyroscope-based tracker, an optical tracker (active or passive), an infrared tracker, an acoustic tracker, a laser tracker, a radio frequency tracker, an inertial tracker, and augmented reality based tracking systems. Other types of trackers may be possible as well.

FIG. 9A illustrates an example embodiment of the face-mounted display device 140 (FMDD) of the system 100 of FIG. 1. FIG. 9B is an illustration of how the FMDD 140 of FIG. 9A is secured on the head of a user. FIG. 9C illustrates an example embodiment of the FMDD 140 of FIG. 9A integrated into a welding helmet 900. The FMDD 140 operatively connects to the PPS 110 and the ST 120 either via wired means or wirelessly. A sensor 122 of the ST 120 may be attached to the FMDD 140 or to the welding helmet 900, in accordance with various embodiments of the present invention, allowing the FMDD 140 and/or welding helmet 900 to be tracked with respect to the 3D spatial frame of reference created by the ST 120.

In accordance with an embodiment of the present invention, the FMDD 140 includes two high-contrast SVGA 3D OLED microdisplays capable of delivering fluid full-motion video in the 2D and frame sequential video modes. Video of the virtual reality environment is provided and displayed on the FMDD 140. A zoom (e.g., 2X) mode may be provided, allowing a user to simulate a cheater lens, for example.

The FMDD 140 further includes two earbud speakers 910, allowing the user to hear simulated welding-related and environmental sounds produced by the system 100. The FMDD 140 may operatively interface to the PPS 110 via wired or wireless means, in accordance with various embodiments of the present invention. In accordance with an embodiment of the present invention, the PPS 110 provides stereoscopic video to the FMDD 140, providing enhanced depth perception to the user. In accordance with an alternate embodiment of the present invention, a user is able to use a control on the MWT 160 (e.g., a button or switch) to call up and select menus and display options on the FMDD 140. This may allow the user to easily reset a weld if he makes a mistake, change certain parameters, or back up a little to re-do a portion of a weld bead trajectory, for example.

FIG. 10 illustrates an example embodiment of a subsystem block diagram of the programmable processor-based subsystem (PPS) 110 of the system 100 of FIG. 1. The PPS 110 includes a central processing unit (CPU) 111 and two graphics processing units (GPU) 115, in accordance with an embodiment of the present invention. The two GPUs 115 are programmed to provide virtual reality simulation of a weld puddle (a.k.a. a weld pool) having real-time molten metal fluidity and heat absorption and dissipation characteristics, in accordance with an embodiment of the present invention.

FIG. 11 illustrates an example embodiment of a block diagram of a graphics processing unit (GPU) 115 of the PPS 110 of FIG. 10. Each GPU 115 supports the implementation of data parallel algorithms. In accordance with an embodiment of the present invention, each GPU 115 provides two video outputs 118 and 119 capable of providing two virtual reality views. Two of the video outputs may be routed to the FMDD 140, rendering the welder's point of view, and a third video output may be routed to the ODD 150, for example, rendering either the welder's point of view or some other point of view. The remaining fourth video output may be routed to a projector, for example. Both GPUs 115 perform the same welding physics computations but may render the virtual reality environment from the same or different points of view. The GPU 115 includes a compute unified device architecture (CUDA) 116 and a shader 117. The CUDA 116 is the computing engine of the GPU 115 which is accessible to software developers through industry standard programming languages. The CUDA 116 includes parallel cores and is used to run the physics model of the weld puddle simulation described herein. The CPU 111 provides real-time welding input data to the CUDA 116 on the GPU 115. The shader 117 is responsible for drawing and applying all of the visuals of the simulation. Bead and puddle visuals are driven by the state of a wexel displacement map which is described later herein. In accordance with an embodiment of the present invention, the physics model runs and updates at a rate of about 30 times per second.

FIG. 12 illustrates an example embodiment of a functional block diagram of the system 100 of FIG. 1. The various functional blocks of the system 100 as shown in FIG. 12 are implemented largely via software instructions and modules running on the PPS 110. The various functional blocks of the system 100 include a physical interface 1201, torch and clamp models 1202, environment models 1203, sound content functionality 1204, welding sounds 1205, stand/table model 1206, internal architecture functionality 1207, calibration functionality 1208, coupon models 1210, welding physics 1211, internal physics adjustment tool (tweaker) 1212, graphical user interface functionality 1213, graphing functionality 1214, student reports functionality 1215, renderer 1216, bead rendering 1217, 3D textures 1218, visual cues functionality 1219, scoring and tolerance functionality 1220, tolerance editor 1221, and special effects 1222.

The internal architecture functionality 1207 provides the higher level software logistics of the processes of the system 100 including, for example, loading files, holding information, managing threads, turning the physics model on, and triggering menus. The internal architecture functionality 1207 runs on the CPU 111, in accordance with an embodiment of the present invention. Certain real-time inputs to the PPS 110 include arc location, gun position, FMDD or helmet position, gun on/off state, and contact made state (yes/no).

The graphical user interface functionality 1213 allows a user, through the ODD 150 using the joystick 132 of the physical user interface 130, to set up a welding scenario. In accordance with an embodiment of the present invention, the set up of a welding scenario includes selecting a language, entering a user name, selecting a practice plate (i.e., a welding coupon), selecting a welding process (e.g., FCAW, GMAW, SMAW) and associated axial spray, pulse, or short arc methods, selecting a gas type and flow rate, selecting a type of stick electrode (e.g., 6010 or 7018), and selecting a type of flux cored wire (e.g., self-shielded, gas-shielded). The set up of a welding scenario also includes selecting a table height, an arm height, an arm position, and an arm rotation of the T/S 170. The set up of a welding scenario further includes selecting an environment (e.g., a background environment in virtual reality space), setting a wire feed speed, setting a voltage level, setting an amperage, selecting a polarity, and turning particular visual cues on or off.

During a simulated welding scenario, the graphing functionality 1214 gathers user performance parameters and provides the user performance parameters to the graphical user interface functionality 1213 for display in a graphical format (e.g., on the ODD 150). Tracking information from the ST 120 feeds into the graphing functionality 1214. The graphing functionality 1214 includes a simple analysis module (SAM) and a whip/weave analysis module (WWAM). The SAM analyzes user welding parameters including welding travel angle, travel speed, weld angle, position, and tip to work distance by comparing the welding parameters to data stored in bead tables. The WWAM analyzes user whipping parameters including dime spacing, whip time, and puddle time. The WWAM also analyzes user weaving parameters including width of weave, weave spacing, and weave timing. The SAM and WWAM interpret raw input data (e.g., position and orientation data) into functionally usable data for graphing. For each parameter analyzed by the SAM and the WWAM, a tolerance window is defined by parameter limits around an optimum or ideal set point input into bead tables using the tolerance editor 1221, and scoring and tolerance functionality 1220 is performed.

The tolerance editor 1221 includes a weldometer which approximates material usage, electrical usage, and welding time. Furthermore, when certain parameters are out of tolerance, welding discontinuities (i.e., welding defects) may occur. The state of any welding discontinuities are processed by the graphing functionality 1214 and presented via the graphical user interface functionality 1213 in a graphical format. Such welding discontinuities include improper weld size, poor bead placement, concave bead, excessive convexity, undercut, porosity, incomplete fusion, slag entrapment, overfill, burnthrough, and excessive spatter. In accordance with an embodiment of the present invention, the level or amount of a discontinuity is dependent on how far away a particular user parameter is from the optimum or ideal set point.

Different parameter limits may be pre-defined for different types of users such as, for example, welding novices, welding experts, and persons at a trade show. The scoring and tolerance functionality 1220 provide number scores depending on how close to optimum (ideal) a user is for a particular parameter and depending on the level of discontinuities or defects present in the weld. The optimum values are derived from real-world data. Information from the scoring and tolerance functionality 1220 and from the graphics functionality 1214 may be used by the student reports functionality 1215 to create a performance report for an instructor and/or a student.

The system 100 is capable of analyzing and displaying the results of virtual welding activity. By analyzing the results, it is meant that system 100 is capable of determining when during the welding pass and where along the weld joints, the user deviated from the acceptable limits of the welding process. A score may be attributed to the user's performance. In one embodiment, the score may be a function of deviation in position, orientation and speed of the mock welding tool 160 through ranges of tolerances, which may extend from an ideal welding pass to marginal or unacceptable welding activity. Any gradient of ranges may be incorporated into the system 100 as chosen for scoring the user's performance. Scoring may be displayed numerically or alpha-numerically. Additionally, the user's performance may be displayed graphically showing, in time and/or position along the weld joint, how closely the mock welding tool traversed the weld joint. Parameters such as travel angle, work angle, speed, and distance from the weld joint are examples of what may be measured, although any parameters may be analyzed for scoring purposes. The tolerance ranges of the parameters are taken from real-world welding data, thereby providing accurate feedback as to how the user will perform in the real world. In another embodiment, analysis of the defects corresponding to the user's performance may also be incorporated and displayed on the ODD 150. In this embodiment, a graph may be depicted indicating what type of discontinuity resulted from measuring the various parameters monitored during the virtual welding activity. While occlusions may not be visible on the ODD 150, defects may still have occurred as a result of the user's performance, the results of which may still be correspondingly displayed, i.e. graphed.

Visual cues functionality 1219 provide immediate feedback to the user by displaying overlaid colors and indicators on the FMDD 140 and/or the ODD 150. Visual cues are provided for each of the welding parameters 151 including position, tip to work distance, weld angle, travel angle, travel speed, and arc length (e.g., for stick welding) and visually indicate to the user if some aspect of the user's welding technique should be adjusted based on the predefined limits or tolerances. Visual cues may also be provided for whip/weave technique and weld bead “dime” spacing, for example. Visual cues may be set independently or in any desired combination.

Calibration functionality 1208 provides the capability to match up physical components in real world space (3D frame of reference) with visual components in virtual reality space. Each different type of welding coupon (WC) is calibrated in the factory by mounting the WC to the arm 173 of the T/S 170 and touching the WC at predefined points (indicated by, for example, three dimples on the WC) with a calibration stylus operatively connected to the ST 120. The ST 120 reads the magnetic field intensities at the predefined points, provides position information to the PPS 110, and the PPS 110 uses the position information to perform the calibration (i.e., the translation from real world space to virtual reality space).

Any particular type of WC fits into the arm 173 of the T/S 170 in the same repeatable way to within very tight tolerances. Therefore, once a particular WC type is calibrated, that WC type does not have to be re-calibrated (i.e., calibration of a particular type of WC is a one-time event). WCs of the same type are interchangeable. Calibration ensures that physical feedback perceived by the user during a welding process matches up with what is displayed to the user in virtual reality space, making the simulation seem more real. For example, if the user slides the tip of a MWT 160 around the corner of a actual WC 180, the user will see the tip sliding around the corner of the virtual WC on the FMDD 140 as the user feels the tip sliding around the actual corner. In accordance with an embodiment of the present invention, the MWT 160 is placed in a pre-positioned jig and is calibrated as well, based on the known jig position.

In accordance with an alternative embodiment of the present invention, “smart” coupons are provided, having sensors on, for example, the corners of the coupons. The ST 120 is able to track the corners of a “smart” coupon such that the system 100 continuously knows where the “smart” coupon is in real world 3D space. In accordance with a further alternative embodiment of the present invention, licensing keys are provided to “unlock” welding coupons. When a particular WC is purchased, a licensing key is provided allowing the user to enter the licensing key into the system 100, unlocking the software associated with that WC. In accordance with another embodiment of the present invention, special non-standard welding coupons may be provided based on real-world CAD drawings of parts. Users may be able to train on welding a CAD part even before the part is actually produced in the real world.

Sound content functionality 1204 and welding sounds 1205 provide particular types of welding sounds that change depending on if certain welding parameters are within tolerance or out of tolerance. Sounds are tailored to the various welding processes and parameters. For example, in a MIG spray arc welding process, a crackling sound is provided when the user does not have the MWT 160 positioned correctly, and a hissing sound is provided when the MWT 160 is positioned correctly. In a short arc welding process, a steady crackling or frying sound is provided for proper welding technique, and a hissing sound may be provided when undercutting is occurring. These sounds mimic real world sounds corresponding to correct and incorrect welding technique.

High fidelity sound content may be taken from real world recordings of actual welding using a variety of electronic and mechanical means, in accordance with various embodiments of the present invention. In accordance with an embodiment of the present invention, the perceived volume and directionality of sound is modified depending on the position, orientation, and distance of the user's head (assuming the user is wearing a FMDD 140 that is tracked by the ST 120) with respect to the simulated arc between the MWT 160 and the WC 180. Sound may be provided to the user via ear bud speakers 910 in the FMDD 140 or via speakers configured in the console 135 or T/S 170, for example.

Environment models 1203 are provided to provide various background scenes (still and moving) in virtual reality space. Such background environments may include, for example, an indoor welding shop, an outdoor race track, a garage, etc. and may include moving cars, people, birds, clouds, and various environmental sounds. The background environment may be interactive, in accordance with an embodiment of the present invention. For example, a user may have to survey a background area, before starting welding, to ensure that the environment is appropriate (e.g., safe) for welding. Torch and clamp models 1202 are provided which model various MWTs 160 including, for example, guns, holders with stick electrodes, etc. in virtual reality space.

Coupon models 1210 are provided which model various WCs 180 including, for example, flat plate coupons, T-joint coupons, butt-joint coupons, groove-weld coupons, and pipe coupons (e.g., 2-inch diameter pipe and 6-inch diameter pipe) in virtual reality space. A stand/table model 1206 is provided which models the various parts of the T/S 170 including an adjustable table 171, a stand 172, an adjustable arm 173, and a vertical post 174 in virtual reality space. A physical interface model 1201 is provided which models the various parts of the welding user interface 130, console 135, and ODD 150 in virtual reality space.

In accordance with an embodiment of the present invention, simulation of a weld puddle or pool in virtual reality space is accomplished where the simulated weld puddle has real-time molten metal fluidity and heat dissipation characteristics. At the heart of the weld puddle simulation is the welding physics functionality 1211 (a.k.a., the physics model) which is run on the GPUs 115, in accordance with an embodiment of the present invention. The welding physics functionality employs a double displacement layer technique to accurately model dynamic fluidity/viscosity, solidity, heat gradient (heat absorption and dissipation), puddle wake, and bead shape, and is described in more detail herein with respect to FIGS. 14A-14C.

The welding physics functionality 1211 communicates with the bead rendering functionality 1217 to render a weld bead in all states from the heated molten state to the cooled solidified state. The bead rendering functionality 1217 uses information from the welding physics functionality 1211 (e.g., heat, fluidity, displacement, dime spacing) to accurately and realistically render a weld bead in virtual reality space in real-time. The 3D textures functionality 1218 provides texture maps to the bead rendering functionality 1217 to overlay additional textures (e.g., scorching, slag, grain) onto the simulated weld bead. For example, slag may be shown rendered over a weld bead during and just after a welding process, and then removed to reveal the underlying weld bead. The renderer functionality 1216 is used to render various non-puddle specific characteristics using information from the special effects module 1222 including sparks, spatter, smoke, arc glow, fumes and gases, and certain discontinuities such as, for example, undercut and porosity.

The internal physics adjustment tool 1212 is a tweaking tool that allows various welding physics parameters to be defined, updated, and modified for the various welding processes. In accordance with an embodiment of the present invention, the internal physics adjustment tool 1212 runs on the CPU 111 and the adjusted or updated parameters are downloaded to the GPUs 115. The types of parameters that may be adjusted via the internal physics adjustment tool 1212 include parameters related to welding coupons, process parameters that allow a process to be changed without having to reset a welding coupon (allows for doing a second pass), various global parameters that can be changed without resetting the entire simulation, and other various parameters.

FIG. 13 is a flow chart of an embodiment of a method 1300 of training using the virtual reality training system 100 of FIG. 1. In step 1310, move a mock welding tool with respect to a welding coupon in accordance with a welding technique. In step 1320, track position and orientation of the mock welding tool in three-dimensional space using a virtual reality system. In step 1330, view a display of the virtual reality welding system showing a real-time virtual reality simulation of the mock welding tool and the welding coupon in a virtual reality space as the simulated mock welding tool deposits a simulated weld bead material onto at least one simulated surface of the simulated welding coupon by forming a simulated weld puddle in the vicinity of a simulated arc emitting from said simulated mock welding tool. In step 1340, view on the display, real-time molten metal fluidity and heat dissipation characteristics of the simulated weld puddle. In step 1350, modify in real-time, at least one aspect of the welding technique in response to viewing the real-time molten metal fluidity and heat dissipation characteristics of the simulated weld puddle.

The method 1300 illustrates how a user is able to view a weld puddle in virtual reality space and modify his welding technique in response to viewing various characteristics of the simulated weld puddle, including real-time molten metal fluidity (e.g., viscosity) and heat dissipation. The user may also view and respond to other characteristics including real-time puddle wake and dime spacing. Viewing and responding to characteristics of the weld puddle is how most welding operations are actually performed in the real world. The double displacement layer modeling of the welding physics functionality 1211 run on the GPUs 115 allows for such real-time molten metal fluidity and heat dissipation characteristics to be accurately modeled and represented to the user. For example, heat dissipation determines solidification time (i.e., how much time it takes for a wexel to completely solidify).

Furthermore, a user may make a second pass over the weld bead material using the same or a different (e.g., a second) mock welding tool and/or welding process. In such a second pass scenario, the simulation shows the simulated mock welding tool, the welding coupon, and the original simulated weld bead material in virtual reality space as the simulated mock welding tool deposits a second simulated weld bead material merging with the first simulated weld bead material by forming a second simulated weld puddle in the vicinity of a simulated arc emitting from the simulated mock welding tool. Additional subsequent passes using the same or different welding tools or processes may be made in a similar manner. In any second or subsequent pass, the previous weld bead material is merged with the new weld bead material being deposited as a new weld puddle is formed in virtual reality space from the combination of any of the previous weld bead material, the new weld bead material, and possibly the underlying coupon material in accordance with certain embodiments of the present invention. Such subsequent passes may be needed to make a large fillet or groove weld, performed to repair a weld bead formed by a previous pass, for example, or may include a hot pass and one or more fill and cap passes after a root pass as is done in pipe welding. In accordance with various embodiments of the present invention, weld bead and base material may include mild steel, stainless steel, aluminum, nickel based alloys, or other materials.

FIGS. 14A-14B illustrate the concept of a welding element (wexel) displacement map 1420, in accordance with an embodiment of the present invention. FIG. 14A shows a side view of a flat welding coupon (WC) 1400 having a flat top surface 1410. The welding coupon 1400 exists in the real world as, for example, a plastic part, and also exists in virtual reality space as a simulated welding coupon. FIG. 14B shows a representation of the top surface 1410 of the simulated WC 1400 broken up into a grid or array of welding elements (i.e., wexels) forming a wexel map 1420. Each wexel (e.g., wexel 1421) defines a small portion of the surface 1410 of the welding coupon. The wexel map defines the surface resolution. Changeable channel parameter values are assigned to each wexel, allowing values of each wexel to dynamically change in real-time in virtual reality weld space during a simulated welding process. The changeable channel parameter values correspond to the channels Puddle (molten metal fluidity/viscosity displacement), Heat (heat absorption/dissipation), Displacement (solid displacement), and Extra (various extra states, e.g., slag, grain, scorching, virgin metal). These changeable channels are referred to herein as PHED for Puddle, Heat, Extra, and Displacement, respectively.

FIG. 15 illustrates an example embodiment of a coupon space and a weld space of the flat welding coupon (WC) 1400 of FIG. 14 simulated in the system 100 of FIG. 1. Points O, X, Y, and Z define the local 3D coupon space. In general, each coupon type defines the mapping from 3D coupon space to 2D virtual reality weld space. The wexel map 1420 of FIG. 14 is a two-dimensional array of values that map to weld space in virtual reality. A user is to weld from point B to point E as shown in FIG. 15. A trajectory line from point B to point E is shown in both 3D coupon space and 2D weld space in FIG. 15.

Each type of coupon defines the direction of displacement for each location in the wexel map. For the flat welding coupon of FIG. 15, the direction of displacement is the same at all locations in the wexel map (i.e., in the Z-direction). The texture coordinates of the wexel map are shown as S, T (sometimes called U, V) in both 3D coupon space and 2D weld space, in order to clarify the mapping. The wexel map is mapped to and represents the rectangular surface 1410 of the welding coupon 1400.

FIG. 16 illustrates an example embodiment of a coupon space and a weld space of a corner (tee joint) welding coupon (WC) 1600 simulated in the system 100 of FIG. 1. The corner WC 1600 has two surfaces 1610 and 1620 in 3D coupon space that are mapped to 2D weld space as shown in FIG. 16. Again, points O, X, Y, and Z define the local 3D coupon space. The texture coordinates of the wexel map are shown as S, T in both 3D coupon space and 2D weld space, in order to clarify the mapping. A user is to weld from point B to point E as shown in FIG. 16. A trajectory line from point B to point E is shown in both 3D coupon space and 2D weld space in FIG. 16. However, the direction of displacement is towards the line X′-O′ as shown in the 3D coupon space, towards the opposite corner as shown in FIG. 16.

FIG. 17 illustrates an example embodiment of a coupon space and a weld space of a pipe welding coupon (WC) 1700 simulated in the system 100 of FIG. 1. The pipe WC 1700 has a curved surface 1710 in 3D coupon space that is mapped to 2D weld space as shown in FIG. 17. Again, points O, X, Y, and Z define the local 3D coupon space. The texture coordinates of the wexel map are shown as S, T in both 3D coupon space and 2D weld space, in order to clarify the mapping. A user is to weld from point B to point E along a curved trajectory as shown in FIG. 17. A trajectory curve and line from point B to point E is shown in 3D coupon space and 2D weld space, respectively, in FIG. 17. The direction of displacement is away from the line Y-O (i.e., away from the center of the pipe). FIG. 18 illustrates an example embodiment of the pipe welding coupon (WC) 1700 of FIG. 17. The pipe WC 1700 is made of a non-ferric, non-conductive plastic and simulates two pipe pieces 1701 and 1702 coming together to form a root joint 1703. An attachment piece 1704 for attaching to the arm 173 of the T/S 170 is also shown.

In a similar manner that a texture map may be mapped to a rectangular surface area of a geometry, a weldable wexel map may be mapped to a rectangular surface of a welding coupon. Each element of the weldable map is termed a wexel in the same sense that each element of a picture is termed a pixel (a contraction of picture element). A pixel contains channels of information that define a color (e.g., red, green, blue, etc.). A wexel contains channels of information (e.g., P, H, E, D) that define a weldable surface in virtual reality space.

In accordance with an embodiment of the present invention, the format of a wexel is summarized as channels PHED (Puddle, Heat, Extra, Displacement) which contains four floating point numbers. The Extra channel is treated as a set of bits which store logical information about the wexel such as, for example, whether or not there is any slag at the wexel location. The Puddle channel stores a displacement value for any liquefied metal at the wexel location. The Displacement channel stores a displacement value for the solidified metal at the wexel location. The Heat channel stores a value giving the magnitude of heat at the wexel location. In this way, the weldable part of the coupon can show displacement due to a welded bead, a shimmering surface “puddle” due to liquid metal, color due to heat, etc. All of these effects are achieved by the vertex and pixel shaders applied to the weldable surface.

In accordance with an embodiment of the present invention, a displacement map and a particle system are used where the particles can interact with each other and collide with the displacement map. The particles are virtual dynamic fluid particles and provide the liquid behavior of the weld puddle but are not rendered directly (i.e., are not visually seen directly). Instead, only the particle effects on the displacement map are visually seen. Heat input to a wexel affects the movement of nearby particles. There are two types of displacement involved in simulating a welding puddle which include Puddle and Displacement. Puddle is “temporary” and only lasts as long as there are particles and heat present. Displacement is “permanent”. Puddle displacement is the liquid metal of the weld which changes rapidly (e.g., shimmers) and can be thought of as being “on top” of the Displacement. The particles overlay a portion of a virtual surface displacement map (i.e., a wexel map). The Displacement represents the permanent solid metal including both the initial base metal and the weld bead that has solidified.

In accordance with an embodiment of the present invention, the simulated welding process in virtual reality space works as follows: Particles stream from the emitter (emitter of the simulated MWT 160) in a thin cone. The particles make first contact with the surface of the simulated welding coupon where the surface is defined by a wexel map. The particles interact with each other and the wexel map and build up in real-time. More heat is added the nearer a wexel is to the emitter. Heat is modeled in dependence on distance from the arc point and the amount of time that heat is input from the arc. Certain visuals (e.g., color, etc.) are driven by the heat. A weld puddle is drawn or rendered in virtual reality space for wexels having enough heat. Wherever it is hot enough, the wexel map liquefies, causing the Puddle displacement to “raise up” for those wexel locations. Puddle displacement is determined by sampling the “highest” particles at each wexel location. As the emitter moves on along the weld trajectory, the wexel locations left behind cool. Heat is removed from a wexel location at a particular rate. When a cooling threshold is reached, the wexel map solidifies. As such, the Puddle displacement is gradually converted to Displacement (i.e., a solidified bead). Displacement added is equivalent to Puddle removed such that the overall height does not change. Particle lifetimes are tweaked or adjusted to persist until solidification is complete. Certain particle properties that are modeled in the system 100 include attraction/repulsion, velocity (related to heat), dampening (related to heat dissipation), direction (related to gravity).

FIGS. 19A-19C illustrate an example embodiment of the concept of a dual-displacement (displacement and particles) puddle model of the system 100 of FIG. 1. Welding coupons are simulated in virtual reality space having at least one surface. The surfaces of the welding coupon are simulated in virtual reality space as a double displacement layer including a solid displacement layer and a puddle displacement layer. The puddle displacement layer is capable of modifying the solid displacement layer.

As described herein, “puddle” is defined by an area of the wexel map where the Puddle value has been raised up by the presence of particles. The sampling process is represented in FIGS. 19A-19C. A section of a wexel map is shown having seven adjacent wexels. The current Displacement values are represented by un-shaded rectangular bars 1910 of a given height (i.e., a given displacement for each wexel). In FIG. 19A, the particles 1920 are shown as round un-shaded dots colliding with the current Displacement levels and are piled up. In FIG. 19B, the “highest” particle heights 1930 are sampled at each wexel location. In FIG. 19C, the shaded rectangles 1940 show how much Puddle has been added on top of the Displacement as a result of the particles. The weld puddle height is not instantly set to the sampled values since Puddle is added at a particular liquification rate based on Heat. Although not shown in FIGS. 19A-19C, it is possible to visualize the solidification process as the Puddle (shaded rectangles) gradually shrink and the Displacement (un-shaded rectangles) gradually grow from below to exactly take the place of the Puddle. In this manner, real-time molten metal fluidity characteristics are accurately simulated. As a user practices a particular welding process, the user is able to observe the molten metal fluidity characteristics and the heat dissipation characteristics of the weld puddle in real-time in virtual reality space and use this information to adjust or maintain his welding technique.

The number of wexels representing the surface of a welding coupon is fixed. Furthermore, the puddle particles that are generated by the simulation to model fluidity are temporary, as described herein. Therefore, once an initial puddle is generated in virtual reality space during a simulated welding process using the system 100, the number of wexels plus puddle particles tends to remain relatively constant. This is because the number of wexels that are being processed is fixed and the number of puddle particles that exist and are being processed during the welding process tend to remain relatively constant because puddle particles are being created and “destroyed” at a similar rate (i.e., the puddle particles are temporary). Therefore, the processing load of the PPS 110 remains relatively constant during a simulated welding session.

In accordance with an alternate embodiment of the present invention, puddle particles may be generated within or below the surface of the welding coupon. In such an embodiment, displacement may be modeled as being positive or negative with respect to the original surface displacement of a virgin (i.e., un-welded) coupon. In this manner, puddle particles may not only build up on the surface of a welding coupon, but may also penetrate the welding coupon. However, the number of wexels is still fixed and the puddle particles being created and destroyed is still relatively constant.

In accordance with alternate embodiments of the present invention, instead of modeling particles, a wexel displacement map may be provided having more channels to model the fluidity of the puddle. Or, instead of modeling particles, a dense voxel map may be modeled. Or, instead of a wexel map, only particles may be modeled which are sampled and never go away. Such alternative embodiments may not provide a relatively constant processing load for the system, however.

Furthermore, in accordance with an embodiment of the present invention, blowthrough or a keyhole is simulated by taking material away. For example, if a user keeps an arc in the same location for too long, in the real world, the material would burn away causing a hole. Such real-world burnthrough is simulated in the system 100 by wexel decimation techniques. If the amount of heat absorbed by a wexel is determined to be too high by the system 100, that wexel may be flagged or designated as being burned away and rendered as such (e.g., rendered as a hole). Subsequently, however, wexel re-constitution may occur for certain welding process (e.g., pipe welding) where material is added back after being initially burned away. In general, the system 100 simulates wexel decimation (taking material away) and wexel reconstitution (i.e., adding material back). Furthermore, removing material in root-pass welding is properly simulated in the system 100.

Furthermore, removing material in root-pass welding is properly simulated in the system 100. For example, in the real world, grinding of the root pass may be performed prior to subsequent welding passes. Similarly, system 100 may simulate a grinding pass that removes material from the virtual weld joint. It will be appreciated that the material removed may be modeled as a negative displacement on the wexel map. That is to say that the grinding pass removes material that is modeled by the system 100 resulting in an altered bead contour. Simulation of the grinding pass may be automatic, which is to say that the system 100 removes a predetermined thickness of material, which may be respective to the surface of the root pass weld bead.

In an alternative embodiment, an actual grinding tool, or grinder, may be simulated that turns on and off by activation of the mock welding tool 160 or another input device. It is noted that the grinding tool may be simulated to resemble a real world grinder. In this embodiment, the user maneuvers the grinding tool along the root pass to remove material responsive to the movement thereof. It will be understood that the user may be allowed to remove too much material. In a manner similar to that described above, holes or other defects (described above) may result if the user grinds away too much material. Still, hard limits or stops may be implemented, i.e. programmed, to prevent the user from removing too much material or indicate when too much material is being removed.

In addition to the non-visible “puddle” particles described herein, the system 100 also uses three other types of visible particles to represent Arc, Flame, and Spark effects, in accordance with an embodiment of the present invention. These types of particles do not interact with other particles of any type but interact only with the displacement map. While these particles do collide with the simulated weld surface, they do not interact with each other. Only Puddle particles interact with each other, in accordance with an embodiment of the present invention. The physics of the Spark particles is setup such that the Spark particles bounce around and are rendered as glowing dots in virtual reality space.

The physics of the Arc particles is setup such that the Arc particles hit the surface of the simulated coupon or weld bead and stay for a while. The Arc particles are rendered as larger dim bluish-white spots in virtual reality space. It takes many such spots superimposed to form any sort of visual image. The end result is a white glowing nimbus with blue edges.

The physics of the Flame particles is modeled to slowly raise upward. The Flame particles are rendered as medium sized dim red-yellow spots. It takes many such spots superimposed to form any sort of visual image. The end result is blobs of orange-red flames with red edges raising upward and fading out. Other types of non-puddle particles may be implemented in the system 100, in accordance with other embodiments of the present invention. For example, smoke particles may be modeled and simulated in a similar manner to flame particles.

The final steps in the simulated visualization are handled by the vertex and pixel shaders provided by the shaders 117 of the GPUs 115. The vertex and pixel shaders apply Puddle and Displacement, as well as surface colors and reflectivity altered due to heat, etc. The Extra (E) channel of the PHED wexel format, as discussed earlier herein, contains all of the extra information used per wexel. In accordance with an embodiment of the present invention, the extra information includes a non virgin bit (true=bead, false=virgin steel), a slag bit, an undercut value (amount of undercut at this wexel where zero equals no undercut), a porosity value (amount of porosity at this wexel where zero equals no porosity), and a bead wake value which encodes the time at which the bead solidifies. There are a set of image maps associated with different coupon visuals including virgin steel, slag, bead, and porosity. These image maps are used both for bump mapping and texture mapping. The amount of blending of these image maps is controlled by the various flags and values described herein.

A bead wake effect is achieved using a 1D image map and a per wexel bead wake value that encodes the time at which a given bit of bead is solidified. Once a hot puddle wexel location is no longer hot enough to be called “puddle”, a time is saved at that location and is called “bead wake”. The end result is that the shader code is able to use the 1D texture map to draw the “ripples” that give a bead its unique appearance which portrays the direction in which the bead was laid down. In accordance with an alternative embodiment of the present invention, the system 100 is capable of simulating, in virtual reality space, and displaying a weld bead having a real-time weld bead wake characteristic resulting from a real-time fluidity-to-solidification transition of the simulated weld puddle, as the simulated weld puddle is moved along a weld trajectory.

In accordance with an alternative embodiment of the present invention, the system 100 is capable of teaching a user how to troubleshoot a welding machine. For example, a troubleshooting mode of the system may train a user to make sure he sets up the system correctly (e.g., correct gas flow rate, correct power cord connected, etc.) In accordance with another alternate embodiment of the present invention, the system 100 is capable of recording and playing back a welding session (or at least a portion of a welding session, for example, N frames). A track ball may be provided to scroll through frames of video, allowing a user or instructor to critique a welding session. Playback may be provided at selectable speeds as well (e.g., full speed, half speed, quarter speed). In accordance with an embodiment of the present invention, a split-screen playback may be provided, allowing two welding sessions to be viewed side-by-side, for example, on the ODD 150. For example, a “good” welding session may be viewed next to a “poor” welding session for comparison purposes.

Importing and Analyzing External Data

In accordance with certain embodiments, external data may be imported into the virtual reality welding system and analyzed to help characterize, for example, a student welder's progress and to aid in the training of the student welder.

One embodiment provides a method of importing and analyzing data. The method includes importing a first data set of welding quality parameters, being representative of a quality of a weld generated by a student welder during a real-world welding activity corresponding to a defined welding process, into a virtual reality welding system. The method also includes comparing a second data set of welding quality parameters stored on the virtual reality simulator, being representative of a quality of a virtual weld generated by the student welder during a simulated welding activity corresponding to the defined welding process on the virtual reality welding system, to the first data set using a programmable processor-based subsystem of the virtual reality welding system. The method further includes generating a numerical comparison score in response to the comparing using the programmable processor-based subsystem of the virtual reality welding system. The numerical comparison score may be representative of a total deviation in weld quality between the first data set and the second data set. The method may also include importing a third data set of welding quality parameters, being representative of a quality of an ideal weld generated by an expert welder during a real-world welding activity corresponding to the defined welding process, into the virtual reality welding system. The expert welder may be a robotic welder or a human welder, for example. The method may further include comparing the second data set to the third data set using the programmable processor-based subsystem of the virtual reality welding system, and generating a numerical student score in response to the comparing using the programmable processor-based subsystem of the virtual reality welding system. The numerical student score may be representative of a total deviation in weld quality from the ideal weld, for example.

FIG. 20 illustrates the concept of importing welding quality parameters 2020 into a virtual reality welding system 100 from a real-world welding machine 2000. The welding quality parameters 2020 may be imported into the virtual reality welding system 100 via wired means or wireless means through a communication device 2010. In accordance with an embodiment, the communication device 2010 is operatively connected to the programmable processor-based subsystem 110 and provides all of the circuitry and/or software for receiving data in a digitally communicated manner (see FIG. 1). For example, the communication device 2010 may include an Ethernet port and Ethernet-capable receiving circuitry. As another example, the communication device 2010 may provide a wireless Bluetooth™ communication connection. Alternatively, the communication device 2010 may be a device that accepts and reads a non-transitory computer-readable medium such as a computer disk or a flash drive data storage device, for example. As a further alternative embodiment, the communication device 2010 may be a modern device providing connection to the internet. Other types of communication devices are possible as well, in accordance with various other embodiments.

Various types of welding quality parameters are discussed in the published U.S. patent application having Ser. No. 13/453,124 which is incorporated herein by reference. However, other types of welding quality parameters may be possible as well, in accordance with other embodiments. The welding quality parameters represent a quality of a weld generated by, for example, a student welder. Quality parameters may be derived from measured or simulated welding parameters, as discussed later herein. Some examples of measured or simulated welding parameters are a count of the measurements taken, a mean voltage, a root mean square voltage, a voltage variance, a mean current, a root mean square current, a current variance, a mean wire feed speed, a root mean square wire feed speed, and a wire feed speed variance. Some examples of welding quality parameters are a quality count standard deviation, a quality voltage average, a quality voltage standard deviation, a quality current average, a quality current standard deviation, a quality voltage variance average, a quality voltage variance standard deviation, a quality current average, a quality current variance standard deviation, a quality wire feed speed average, a quality wire feed speed standard deviation, a quality wire feed speed variance average, and a quality wire feed speed variance standard deviation.

FIG. 21 is a flow chart of an embodiment of a method 2100 to compare a student welder's real-world welding activity to the student welder's virtual welding activity. In step 2110, a first data set of welding quality parameters, being representative of a quality of a weld generated by a student welder during a real-world welding activity corresponding to a defined welding process, is imported into the virtual reality welding system 100. In step 2120, a second data set of welding quality parameters stored on the virtual reality welding system 100, being representative of a quality of a virtual weld generated by the student welder during a simulated welding activity corresponding to the defined welding process on the virtual reality welding system 100, is compared to the first data set using the programmable processor-based subsystem 110 of the virtual reality welding system 100. In step 2130, a numerical comparison score is generated in response to the comparing step using the programmable processor-based subsystem 110 of the virtual reality welding system 100.

The method 2100 of FIG. 21 may represent the situation where the student welder, after having trained to perform the defined welding process on the virtual reality welding system 100, transitions to a corresponding real-world welding system 2000 and performs the same defined welding process in the real-world, actually creating a real weld. Welding quality parameters are generated and stored in both the virtual situation and the real-world situation. The method 2100 allows the student welder to compare his welding performance in the real-world to his welding performance in the virtual world, with respect to the defined welding process. Examples of defined welding processes include gas metal arc welding (GMAW) processes, stick welding processes, flux cored arc welding (FCAW) processes, and gas tungsten arc welding (GTAW) processes. Other types of defined welding processes are possible as well, in accordance with various other embodiments.

The numerical comparison score may be representative of a total deviation in weld quality between the first data set and the second data set. Alternatively, the numerical comparison score may be representative of a total closeness in weld quality of the first data set to the second data set. For example, the numerical comparison score may be calculated by taking a difference between each corresponding weld quality parameter from the virtual welding activity and the real-world welding activity, weighting each difference, and summing the weighted differences. Other methods of generating the numerical comparison score are possible as well, in accordance with various other embodiments. For example, the published U.S. patent application having Ser. No. 13/453,124 which is incorporated herein by reference discloses methods of calculating such scores. As one example, each quality value may be compared to an expected quality value to determine if a difference between the quality value and the expected quality value exceeds a predetermined threshold. If the difference exceeds the threshold, the quality value may be weighted with a magnitude weight based on the difference, and the quality value may be weighted with a time contribution weight based on a time contribution of the state to its wave shape. All of the quality values, including any weighted quality values, obtained during said arc welding process may be used to determine the numerical score. Furthermore, the numerical comparison score may be normalized to a range of 0% to 100%, for example, where 0% represents a maximum deviation and 100% represents a minimum deviation.

FIG. 22 is a flow chart of an embodiment of a method 2200 to compare a student welder's virtual welding activity to an expert welder's real-world welding activity. In step 2210, a third data set of welding quality parameters, being representative of a quality of an ideal weld generated by an expert welder during a real-world welding activity corresponding to a defined welding process, is imported into the virtual reality welding system 100. The expert welder may be an experienced human welder or a programmed robotic welder, for example. In step 2220, a second data set of welding quality parameters stored on the virtual reality system, being representative of a quality of a virtual weld generated by the student welder during a simulated welding activity corresponding to the defined welding process on the virtual reality welding system 100, is compared to the third data set using the programmable processor-based subsystem 110 of the virtual reality welding system 100. In step 2230, a numerical student score is generated in response to the comparing step using the programmable processor-based subsystem 110 of the virtual reality welding system 100.

The method 2200 of FIG. 22 may represent the situation where the student welder is learning to perform the defined welding process using the virtual reality welding system 100, and wants to know how much progress he is making with respect to an ideal weld created in the real-world. Again, welding quality parameters are generated and stored in both the virtual situation and the real-world situation. The method 2200 allows the student welder to compare his welding performance in the virtual world to an expert's welding performance in the real world, with respect to the defined welding process. The numerical student score, similar to the numerical comparison score, may be representative of a total deviation in weld quality from the ideal weld. Alternatively, the numerical student score may be representative of a total closeness in weld quality to the ideal weld. For example, the numerical student score may be calculated by taking a difference between each corresponding weld quality parameter from the student's virtual welding activity and the expert's real-world welding activity, weighting each difference, and summing the weighted differences. Other methods of generating the numerical student score are possible as well, in accordance with various other embodiments. Similar to the numerical comparison score, the numerical student score may be normalized to a range of 0% to 100%.

Another embodiment provides a method of importing and analyzing data. The method includes importing a first data set of measured welding parameters, generated during a real-world welding activity corresponding to a defined welding process performed by an expert welder using a real-world welding machine, into a virtual reality welding system. The expert welder may be a robotic welder or a human welder. The method also includes storing a second data set of simulated welding parameters, generated during a simulated welding activity corresponding to the defined welding process as performed by a student welder using the virtual reality welding system, on the virtual reality welding system. The method further includes calculating a plurality of expert welding quality parameters based on the first data set using the programmable processor-based subsystem of the virtual reality welding system. The method also includes calculating a plurality of student welding quality parameters based on the second data set using the programmable processor-based subsystem of the virtual reality welding system. The method may also include comparing the plurality of expert welding quality parameters to the plurality of student welding quality parameters using the programmable processor-based subsystem of the virtual reality welding system. The method may further include generating a numerical student score in response to the comparing using the programmable processor-based subsystem of the virtual reality welding system. The numerical student score may be representative of a total deviation in weld quality from the weld of ideal quality, for example.

FIG. 23 illustrates the concept of importing measured welding parameters into a virtual reality welding system 100 from a real-world welding machine 2000. The measured welding parameters 2320 may be imported into the virtual reality welding system 100 via wired means or wireless means through the communication device 2010. In accordance with an embodiment, the communication device 2010 is operatively connected to the programmable processor-based subsystem 110 and provides all of the circuitry and/or software for receiving data in a digitally communicated manner (see FIG. 1). For example, the communication device 2010 may include an Ethernet port and Ethernet-capable receiving circuitry. As another example, the communication device 2010 may provide a wireless Bluetooth™ communication connection. Alternatively, the communication device 2010 may be a device that accepts and reads a non-transitory computer-readable medium such as a computer disk or a flash drive data storage device, for example. As a further alternative embodiment, the communication device 2010 may be a modern device providing connection to the internet. Other types of communication devices are possible as well, in accordance with various embodiments.

Various types of measured welding parameters are discussed in the published U.S. patent application having Ser. No. 13/453,124 which is incorporated herein by reference. However, other types of measured welding parameters may be possible as well, in accordance with other embodiments. The measured welding parameters are representative of actual welding parameters that occur during a welding activity for a defined welding process where a welding wire advances toward a workpiece to create a weld. In accordance with an embodiment, quality parameters may be derived from measured welding parameters. Some examples of measured welding parameters are a count of the measurements taken, a mean voltage, a root mean square voltage, a voltage variance, a mean current, a root mean square current, and a current variance.

FIG. 24 is a flow chart of a first embodiment of a method 2400 of generating a plurality of student welding quality parameters and a numerical student score. In step 2410, a first data set of measured welding parameters, generated during a real-world welding activity corresponding to a defined welding process performed by an expert welder using the real-world welding machine 2000, is imported into the virtual reality welding system 100. The expert welder may be an experienced human welder or a robotic welder, for example. In step 2420, a second data set of simulated welding parameters, generated during a simulated welding activity corresponding to the defined welding process as performed by a student welder using the virtual reality welding system 100, is stored on the virtual reality welding system 100. The simulated welding parameters correspond to the measured welding parameters, but are generated in the virtual reality welding system 100 as part of the welding simulation, as opposed to in the real-world welding machine. In the virtual reality welding system 100, welding parameters (e.g., current and derivations thereof, voltage and derivations thereof, wire feed speed and derivations thereof) are simulated as part of simulating a virtual weld puddle having real-time molten metal fluidity and heat dissipation characteristics, for example.

In step 2430, a plurality of expert welding quality parameters are calculated based on the first data set using the programmable processor-based subsystem 110 of the virtual reality welding system 100. In step 2440, a plurality of student welding quality parameters are calculated based on the second data set using the programmable processor-based subsystem 110 of the virtual reality welding system 100. The calculating of quality parameters based on measured welding parameters is disclosed in the published U.S. patent application having Ser. No. 13/453,124 which is incorporated herein by reference. The calculating of quality parameters based on simulated welding parameters may be performed in a similar manner.

In the method 2400, the student welding quality parameters are derived from the simulated welding parameters that were generated by the virtual reality welding system during the student welding activity, and the measured welding parameters that were generated by an expert during the real-world welding activity and imported from the real-world welding machine. Therefore, the student welding quality parameters are representative of the student's performance in virtual reality space with respect to an expert welder's performance in the real world.

In step 2450, the plurality of expert welding quality parameters are compared to the plurality of student welding quality parameters using the programmable processor-based subsystem 110 of the virtual reality welding system 100. In step 2460, a numerical student score is calculated in response to the comparing step using the programmable processor-based subsystem 110 of the virtual reality welding system 100. The numerical student score may be representative of a total deviation in weld quality from the ideal weld. Alternatively, the numerical student score may be representative of a total closeness in weld quality to the ideal weld. For example, the numerical student score may be calculated by taking a difference between each corresponding weld quality parameter of the student welding quality parameters and the expert welding quality parameters, weighting each difference, and summing the weighted differences. Other methods of generating the numerical student score are possible as well, in accordance with various other embodiments. Again, the numerical student score may be normalized to a range of 0% to 100%, for example.

A further embodiment provides a method of analyzing simulated welding parameters. The method includes storing a first data set of simulated welding parameters, generated during a first simulated welding activity corresponding to a defined welding process performed by an expert welder using a virtual reality welding system, on the virtual reality welding system. The expert welder may be a robotic welder or a human welder, for example. The method also includes storing a second data set of simulated welding parameters, generated during a second simulated welding activity corresponding to the defined welding process as performed by a student welder using the virtual reality welding system, on the virtual reality welding system. The method further includes calculating a plurality of expert welding quality parameters based on the first data set using a programmable processor-based subsystem of the virtual reality system. The method further includes calculating a plurality of student welding quality parameters based on the second data set using a programmable processor-based subsystem of the virtual reality system. The method may further include comparing the plurality of expert welding quality parameters to the plurality of student welding quality parameters using the programmable processor-based subsystem of the virtual reality welding system. The method may also include generating a numerical student score in response to the comparing using the programmable processor-based subsystem of the virtual reality welding system. The numerical score may be representative of a total deviation in weld quality from the weld of ideal quality, for example.

FIG. 25 is a flow chart of a second embodiment of a method 2500 of generating a plurality of student welding quality parameters and a numerical student score. In step 2510, a first data set of simulated welding parameters, generated during a first simulated welding activity corresponding to a defined welding process performed by an expert welder using the virtual reality welding system 100, is stored on the virtual reality welding system 100. The expert welder may be an experienced human welder or a robotic welder, for example. In step 2520, a second data set of simulated welding parameters, generated during a simulated welding activity corresponding to the defined welding process as performed by a student welder using the virtual reality welding system 100, is stored on the virtual reality welding system 100. The simulated welding parameters are generated in the virtual reality welding system 100 as part of the welding simulation. In step 2530, a plurality of expert welding quality parameters are calculated based on the first data set using the programmable processor-based subsystem 110 of the virtual reality welding system 100. In step 2540, a plurality of student welding quality parameters are calculated based on the second data set using the programmable processor-based subsystem 110 of the virtual reality welding system 100. The calculating of quality parameters, whether measured are simulated, is disclosed in the published U.S. patent application having Ser. No. 13/453,124 which is incorporated herein by reference.

In the method 2500, the welding quality parameters are derived from the simulated welding parameters generated by the virtual reality welding system during the expert welding activity and the student welding activity. Therefore, the student welding quality parameters are representative of the student's performance in virtual reality space and the expert welding quality parameters are representative of the expert's performance in virtual reality space. The student welding quality parameters may next be compared to the expert welding quality parameters.

In step 2550, the plurality of expert welding quality parameters are compared to the plurality of student welding quality parameters using the programmable processor-based subsystem 110 of the virtual reality welding system 100. In step 2560, a numerical student score is calculated in response to the comparing step using the programmable processor-based subsystem 110 of the virtual reality welding system 100. The numerical student score may be representative of a total deviation in weld quality from the ideal weld. Alternatively, the numerical student score may be representative of a total closeness in weld quality to the ideal weld. For example, the numerical student score may be calculated by taking a difference between each corresponding weld quality parameter of the student welding quality parameters and the expert welding quality parameters, weighting each difference, and summing the weighted differences. Other methods of generating a numerical student score are possible as well, in accordance with various other embodiments.

Another embodiment provides a method of importing and analyzing data. The method includes importing a digital model representative of a welded custom assembly into a virtual reality welding system. The method also includes analyzing the digital model to segment the digital model into a plurality of sections using a programmable processor-based subsystem of the virtual reality welding system, wherein each section of the plurality of sections corresponds to a single weld joint type of the welded custom assembly. The method further includes matching each section of the plurality of sections to a virtual welding coupon of a plurality of virtual welding coupons modeled in the virtual reality welding system using the programmable processor-based subsystem of the virtual reality welding system. The method may also include generating a virtual welding training program that uses the virtual welding coupons corresponding to the matched sections of the digital model representative of the welded custom assembly using the programmable processor-based subsystem of the virtual reality welding system. Each of the virtual welding coupons may correspond to a mock welding coupon of the virtual reality welding system. The single weld joint type may include one of a butt joint, a tee joint, a corner joint, an edge joint, or a lap joint, for example. Other single weld joint types are possible as well, in accordance with various other embodiments.

FIG. 26 illustrates the concept of importing a digital model 2600 representative of a welded custom assembly into the virtual reality welding system 100. The welded custom assembly may correspond to the final assembled product of a plurality of metal parts that are welded together. A manufacturer may have many such final assembled products to produce and, therefore, may need to train one or more welders to efficiently and reliably weld the metal parts together. In accordance with an embodiment, the digital model 2600 is a computer-aided design (CAD) model of a custom welded assembly represented in three-dimensional space, for example. Other types of digital models may be possible as well, in accordance with various other embodiments of the present invention. For example, a digital model may correspond to a custom welded assembly shown in multiple two-dimensional views, as on a blue print. The term “digital model” as used herein refers to data and/or instructions that are in a digital format (e.g., a digital electronic format stored on a computer-readable medium) that may be read by a computer-based or processor-based apparatus such as the virtual reality welding system 100.

The digital model 2600 may be imported into the virtual reality welding system 100 via wired means or wireless means through a communication device 2010. In accordance with an embodiment, the communication device 2010 is operatively connected to the programmable processor-based subsystem 110 and provides all of the circuitry and/or software for receiving data in a digitally communicated manner (see FIG. 1). For example, the communication device 2010 may include an Ethernet port and Ethernet-capable receiving circuitry. As another example, the communication device 2010 may provide a wireless Bluetooth™ communication connection. Alternatively, the communication device 2010 may be a device that accepts and reads a non-transitory computer-readable medium such as a computer disk or a flash drive data storage device, for example. As a further alternative embodiment, the communication device 2010 may be a modern device providing connection to the internet. Other types of communication devices are possible as well, in accordance with various other embodiments.

FIG. 27 illustrates the concept of matching sections of a digital model 2600 representative of a welded custom assembly 2700 to a plurality of welding coupons 2705. For example, the welded custom assembly 2700 may be made up of a plurality of different weld joint types, joining together a plurality of different metal parts. In accordance with an embodiment, the virtual reality welding system 100 is configured to (i.e., programmed to) analyze the digital model 2600 of the custom welded assembly 2700 and segment the digital model 2600 into a plurality of sections, where each section corresponds to a weld joint type of the welded custom assembly 2700. Each section may be matched (or attempted to be matched) to one of the plurality of welding coupons 2705 as discussed with respect to the method 2800 of FIG. 28. For example, a section 2701 of the welded custom assembly 2700, as represented in the digital model 2600, may be matched to a welding coupon 2706.

In accordance with an embodiment, each welding coupon of the plurality of welding coupons 2705 is modeled in the virtual reality welding system 100 in virtual reality space and also exists as a mock welding coupon (e.g., a plastic part) that may be used by a user along with a mock welding tool during a virtual welding activity. The plurality of welding coupons may correspond to coupons having a butt joint, a tee joint, a corner joint, an edge joint, or a lap joint, for example. Welding coupons having other types of joints are possible as well, in accordance with various other embodiments.

FIG. 28 is a flowchart of an embodiment of a method 2800 to generate a virtual welding training program for a welded custom assembly 2700. In step 2810, a digital model 2600 representative of a welded custom assembly 2700 is imported into the virtual reality welding system 100, as illustrated in FIG. 26. In step 2820, the digital model is analyzed using the programmable processor-based subsystem 110 of the virtual reality welding system 100 to segment the digital model 2600 into a plurality of sections, wherein each section of the plurality of sections corresponds to a single weld joint type of the welded custom assembly.

In accordance with an embodiment, the analyzing includes recognizing features in the digital model that correspond to single weld joint types of the welded custom assembly using feature identification techniques. A feature, in computer aided design (CAD) model, may be a region of an assembly with some particular geometric or topological patterns that may relate to, for example, shape, functional, or manufacturing information. In feature recognition, the idea is to algorithmically extract higher level entities (e.g. manufacturing features) from lower level elements (e.g. surfaces, edges, etc.) of a CAD model. The exact types of algorithms used may be chosen and implemented with sound engineering judgment, based on the types of welded custom assemblies expected to be encountered.

In step 2830, each section of the plurality of sections is matched (or attempted to be matched) to a virtual welding coupon of a plurality of virtual welding coupons 2705 modeled in the virtual reality welding system 100 using the programmable processor-based subsystem 110 of the virtual reality welding system 100. In accordance with an embodiment, the feature matching includes using convolution masks or templates, tailored to specific features of the welding coupons. The output of the convolution process is highest at locations where a section matches the mask structure of a welding coupon. The exact types of matching techniques used may be chosen and implemented with sound engineering judgment, based on the types of welded custom assemblies expected to be encountered.

In step 2840, a virtual welding training program is generated that uses the virtual welding coupons corresponding to the matched sections of the digital model 2600 of the custom welded assembly 2700 using the programmable processor-based subsystem 110 of the virtual reality welding system 100. For example, the virtual welding training program may include a sequence of welding steps that direct a welder with respect to how to practice welding of the assembly 2700, using the matched welding coupons, in a particular order.

In summary, a real-time virtual reality welding system is disclosed. The system includes a programmable processor-based subsystem, a spatial tracker operatively connected to the programmable processor-based subsystem, at least one mock welding tool capable of being spatially tracked by the spatial tracker, and at least one display device operatively connected to the programmable processor-based subsystem. The system is capable of simulating, in virtual reality space, a weld puddle having real-time molten metal fluidity and heat dissipation characteristics. The system is further capable of importing data into the virtual reality welding system and analyzing the data to characterize a student welder's progress and to provide training.

While the invention has been described with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from its scope. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.

Claims

1. A method for simulating welding activity, said method comprising: optically tracking a movement and orientation of a welding tool relative to a welding coupon during a simulated welding operation;receiving information related to said movement and orientation of said welding tool;modeling, during said simulated welding operation, a simulated welding surface for said welding coupon;displaying said simulated welding surface on each of a first display device and a second display device, where said first display device is disposed in a helmet;modeling, during said simulated welding operation, a simulated welding arc between an emitting end of said welding tool and said simulated welding surface;modeling a simulated weld puddle generated from a simulated deposition of weld material, wherein said simulated weld puddle has a plurality of welding elements, each welding element having a plurality of values, and where at least some of said values change dynamically during said simulated welding operation based on said movement and orientation of said welding tool, wherein said modeling of said simulated weld puddle includes to model real-time molten metal fluidity and real-time heat dissipation characteristics of said weld puddle;displaying said simulated welding operation, including said simulated weld puddle and said simulated deposition of said weld material, on either one of or both of said first and second display devices; anddisplaying a simulated completed weld bead on either one of or both of said first and second display devices where said simulated completed weld bead is based on a cooling threshold for a transition of said welding elements from a molten state to a solid state and on said movement and orientation of said welding tool.
2. The method of claim 1, wherein said optically tracking uses an optical tracker that is mounted on said helmet.
3. The method of claim 1, said method further comprises playing simulated welding sounds from a speaker in said helmet in real-time with the simulated welding operation.
4. The method of claim 1, further comprising: determining at least one welding parameter during said simulated welding operation; andcomparing said at least one determined welding parameter to a stored value for said at least one determined welding parameter; anddisplaying said comparison on said second display device.
5. The method of claim 4, wherein said displaying of said comparison is in graphical form.
6. The method of claim 1, further comprising: determining at least one welding parameter during said simulated welding operation;comparing said at least one determined welding parameter to a tolerance window defined by limits around a setpoint for said at least one determined welding parameter; anddisplaying said comparison on said second display device.
7. The method of claim 6, wherein said displaying of said comparison is in graphical form.
8. The method of claim 1, further comprising: determining a plurality of welding parameters during said simulated welding operation; andproviding a score for each of said plurality of determined welding parameters based on a comparison between said plurality of determined welding parameters and a desired value for each of said plurality of determined welding parameters, respectively.
9. The method of claim 1, further comprising: determining a plurality of welding parameters during said simulated welding operation; anddetermining a presence of a discontinuity within said completed simulated weld bead, where said discontinuity is at least one of spatter and porosity.
10. The method of claim 9, further comprising: displaying, on either of or both of said first and said second display devices, said discontinuity during said simulated welding operation, in real time, and in said completed simulated weld bead.
11. The method of claim 1, further comprising: generating and displaying on said first display device at least one welding effect, which is one of simulated welding sparks, simulated welding spatter, simulated arc glow and simulated porosity during said simulated welding operation, and where said at least one welding effect is displayed, in real time, based on at least one simulated welding parameter.
12. The method of claim 1, further comprising: changing at least some of said values of at least some of said welding elements of said completed simulated weld bead when said welding tool is used to make a second simulated weld bead over said completed simulated weld bead, and where each of said completed simulated weld bead and said second simulated weld bead are displayed on said first and second display devices at the same time.
13. A method for simulating welding activity, comprising: tracking a movement and orientation of a welding tool relative to a welding coupon using at least one optical sensor during a simulated welding operation;receiving information related to said movement and orientation of said welding tool;modeling, during said simulated welding operation, a simulated welding surface for said welding coupon;displaying said simulated welding surface on each of a first display device and a second display device, where said first display device is disposed in a helmet;modeling a simulated a weld puddle have real-time molten metal fluidity and real-time heat dissipation characteristics during said simulated welding operation, wherein said simulated weld puddle has a plurality of welding elements, where each of said welding elements has a first value representative of heat;dynamically changing said first value for at least some of said welding elements during said simulated welding operation to display on each of said first and second display devices simulated deposition of material into said simulated weld puddle, and where said dynamic changes are based on said movement and orientation of said welding tool; anddisplaying a simulated completed weld bead on said first and second display devices which is based on said movement and orientation of said welding tool, and based on a cooling threshold value for a transition of said welding elements from a molten state to a solid state,wherein when said first value of any of said welding elements is higher than said cooling threshold value said first and second display devices display said welding elements in a simulated fluid state and when said first value is lower than said cooling threshold value said first and second display devices display said welding elements in a simulated solid state.
14. A method for simulating welding activity, comprising: tracking a movement and orientation of a welding tool for a simulated welding operation relative to a welding coupon using at least one optical sensor;displaying, on a first display device which is disposed in a helmet, said movement and orientation of said welding tool and said simulated welding operation;displaying, on a second display device, said movement and orientation of said welding tool and said simulated welding operation;receiving information related to said movement and orientation of said welding tool and using said information to determine a plurality of welding parameters;displaying, on said second display device, said plurality of welding parameters, in real-time, during said simulated welding operation;modeling, during said simulated welding operation, a simulated welding surface for said welding coupon;displaying said simulated welding surface on each of said first and second display devices during said simulated welding operation;modeling, during said simulated welding operation, a simulated welding arc between an emitting end of said welding tool and said simulated welding surface;modeling a simulated weld puddle having real-time molten metal fluidity and real-time heat dissipation characteristics during said simulated welding operation, wherein said simulated weld puddle has a plurality of welding elements, where each of said welding elements has a value representative of heat;dynamically changing said value of at least some of said welding elements during said simulated welding operation to display on each of said first and second display devices a simulated deposition of material into said simulated weld puddle, and where said dynamic changes are based on said movement and orientation of said welding tool;displaying a simulated completed weld bead on said first and second display devices which is based on said movement and orientation of said welding tool, and based on a cooling threshold value for a transition of said welding elements from a molten state to a solid state; anddisplaying at least one of said plurality of welding parameters in graphical form in real time during said simulated welding operation on said second display device.
15. The method of claim 14, wherein said helmet further comprises an audio speaker, and said method further comprises simulating welding sounds in real-time with the simulated welding operation.
16. The method of claim 14, wherein said plurality of welding parameters include weld angle, travel angle, and travel speed.
17. The method of claim 14, further comprising: comparing at least one of said plurality of welding parameters to a tolerance window defined by limits around a setpoint for said at least one welding parameter; anddisplaying said comparison on said second display device.
18. The method of claim 14, further comprising: providing a score for each of said plurality of welding parameters based on a comparison between said plurality of welding parameters and a desired value for each of said plurality of welding parameters, respectively.
19. The method of claim 18, further comprising: displaying, on each of said first and second display devices, a discontinuity during said simulated welding operation, in real time, and in said simulated completed weld bead.
20. The method of claim 14, further comprising: generating and displaying on said first display device at least one welding effect, which is one of simulated welding sparks, simulated welding spatter, simulated arc glow and simulated porosity during said simulated welding operation, and where said at least one welding effect is displayed, in real time, based on said movement and orientation of said welding tool.
21. A method for simulating welding activity, said method comprising: tracking a welding tool to determine a movement and orientation of said welding tool relative to a welding coupon in a simulated welding operation;displaying, on each of a first display device and a second display device, said simulated welding operation, where said displaying includes displaying said movement and orientation of said welding tool;modeling, during said simulated welding operation, a simulated welding surface on said welding coupon;displaying said simulated welding surface on each of said first display device and said second display device;modeling, during said simulated welding operation, a simulated welding arc between an end of said welding tool and said simulated welding surface;using a plurality of welding elements to model a simulated weld puddle having real-time molten metal fluidity and real-time heat dissipation characteristics during said simulated welding operation, where each of said plurality of said welding elements has at least one value representative of heat which changes dynamically during said simulated welding operation based on said movement and said orientation of said welding tool;modeling a simulated weld bead based on said movement and orientation of said welding tool, and based on a cooling threshold value for a transition of said first values for said welding elements from a molten state to a solid state; anddisplaying said simulated weld bead on said first display device and said second display device.
22. The method of claim 21, wherein said tracking is performed optically using optical sensors.
23. The method of claim 21, wherein said first display device is integrated into a helmet comprising an audio speaker, and further comprising simulating welding sounds in real-time with the simulated welding operation.
24. The method of claim 21, further comprising: determining at least one welding parameter during said simulated welding operation; andcomparing said at least one determined welding parameter to a stored value for said at least one determined welding parameter; anddisplaying said comparison on said second display device.
25. The method of claim 24, wherein said displaying of said comparison is in graphical form.
26. The method of claim 21, further comprising: determining at least one welding parameter during said simulated welding operation;comparing said at least one determined welding parameter to a tolerance window defined by limits around a setpoint for said at least one determined welding parameter; anddisplaying said comparison on said second display device.
27. The method of claim 26, wherein said displaying of said comparison is in graphical form.
28. The method of claim 21, further comprising: determining a plurality of welding parameters during said simulated welding operation; andproviding a score for each of said plurality of determined welding parameters based on a comparison between said plurality of determined welding parameters and a desired value for each of said plurality of determined welding parameters, respectively.
29. The method of claim 21, further comprising: determining a plurality of welding parameters during said simulated welding operation;determining a presence of a discontinuity within said simulated weld bead, where said discontinuity is a presence of at least one of spatter and porosity; anddisplaying, on said first display device, said discontinuity during said simulated welding operation, in real time, and in said simulated weld bead.
30. The method of claim 21, further comprising: generating and displaying on said first display device at least one welding effect, which is one of simulated welding sparks, simulated welding spatter, simulated arc glow and simulated porosity during said simulated welding operation, and where said at least one welding effect is displayed, in real time.

CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY REFERENCE

This U.S. patent application claims priority to and is a continuation (CON) of U.S. application Ser. No. 14/821,051, filed Aug. 7, 2015, which is a continuation (CON) of U.S. application Ser. No. 13/792,309, filed on Mar. 11, 2013, now U.S. Pat. No. 9,196,169, which is a continuation-in-part (CIP) patent application of U.S. patent application Ser. No. 12/501,257 filed on Jul. 10, 2009, now U.S. Pat. No. 8,747,116, the disclosures of which are incorporated herein by reference in their entirety. U.S. patent application Ser. No. 12/501,257 claims priority to and the benefit of U.S. Provisional Patent Application Ser. No. 61/090,794 filed on Aug. 21, 2008, which is incorporated herein by reference in its entirety. The Published U.S. patent application having Ser. No. 13/453,124 and filed on Apr. 23, 2012 is incorporated herein by reference in its entirety.

US Referenced Citations (372)

Number	Name	Date	Kind
317063	Wittenstrom	May 1885	A
428459	Coffin	May 1890	A
483428	Coffin	Sep 1892	A
1159119	Springer	Nov 1915	A
D140630	Garibay	Mar 1945	S
D142377	Dunn	Sep 1945	S
D152049	Welch, Jr.	Dec 1948	S
2681969	Burke	Jun 1954	A
D174208	Abildgaard	Mar 1955	S
2728838	Barnes	Dec 1955	A
D176942	Cross	Feb 1956	S
2894086	Rizer	Jul 1959	A
3035155	Hawk	May 1962	A
3059519	Stanton	Oct 1962	A
3356823	Waters et al.	Dec 1967	A
3555239	Kerth	Jan 1971	A
3621177	McPherson et al.	Nov 1971	A
3654421	Streetman et al.	Apr 1972	A
3739140	Rotilio	Jun 1973	A
3866011	Cole	Feb 1975	A
3867769	Schow et al.	Feb 1975	A
3904845	Minkiewicz	Sep 1975	A
3988913	Metcalfe et al.	Nov 1976	A
D243459	Bliss	Feb 1977	S
4024371	Drake	May 1977	A
4041615	Whitehill	Aug 1977	A
D247421	Driscoll	Mar 1978	S
4124944	Blair	Nov 1978	A
4132014	Schow	Jan 1979	A
4237365	Lambros et al.	Dec 1980	A
4280041	Kiessling et al.	Jul 1981	A
4280042	Berger et al.	Jul 1981	A
4280137	Ashida et al.	Jul 1981	A
4314125	Nakamura	Feb 1982	A
4354087	Osterlitz	Oct 1982	A
4359622	Dostoomian et al.	Nov 1982	A
4375026	Kearney	Feb 1983	A
4410787	Kremers et al.	Oct 1983	A
4429266	Tradt	Jan 1984	A
4452589	Denison	Jun 1984	A
D275292	Bouman	Aug 1984	S
D277761	Korovin et al.	Feb 1985	S
4525619	Ide et al.	Jun 1985	A
D280329	Bouman	Aug 1985	S
4611111	Baheti et al.	Sep 1986	A
4616326	Meier et al.	Oct 1986	A
4629860	Lindbom	Dec 1986	A
4677277	Cook et al.	Jun 1987	A
4680014	Paton et al.	Jul 1987	A
4689021	Vasiliev et al.	Aug 1987	A
4707582	Beyer	Nov 1987	A
4716273	Paton et al.	Dec 1987	A
D297704	Bulow	Sep 1988	S
4867685	Brush et al.	Sep 1989	A
4877940	Bangs et al.	Oct 1989	A
4897521	Burr	Jan 1990	A
4907973	Hon	Mar 1990	A
4931018	Herbst et al.	Jun 1990	A
4973814	Kojima et al.	Nov 1990	A
4998050	Nishiyama et al.	Mar 1991	A
5034593	Rice et al.	Jul 1991	A
5061841	Richardson	Oct 1991	A
5089914	Prescott	Feb 1992	A
5192845	Kirmsse et al.	Mar 1993	A
5206472	Myking et al.	Apr 1993	A
5266930	Ichikawa et al.	Nov 1993	A
5285916	Ross	Feb 1994	A
5305183	Teynor	Apr 1994	A
5320538	Baum	Jun 1994	A
5337611	Fleming et al.	Aug 1994	A
5360156	Ishizaka et al.	Nov 1994	A
5360960	Shirk	Nov 1994	A
5370071	Ackermann	Dec 1994	A
D359296	Witherspoon	Jun 1995	S
5424634	Goldfarb et al.	Jun 1995	A
5436638	Bolas et al.	Jul 1995	A
5464957	Kidwell et al.	Nov 1995	A
D365583	Viken	Dec 1995	S
5562843	Yasumoto	Oct 1996	A
5670071	Ueyama et al.	Sep 1997	A
5676503	Lang	Oct 1997	A
5676867	Van Allen	Oct 1997	A
5708253	Bloch et al.	Jan 1998	A
5710405	Solomon et al.	Jan 1998	A
5719369	White et al.	Feb 1998	A
D392534	Degen et al.	Mar 1998	S
5728991	Takada et al.	Mar 1998	A
5751258	Fergason et al.	May 1998	A
D395296	Kaye et al.	Jun 1998	S
D396238	Schmitt	Jul 1998	S
5781258	Dabral et al.	Jul 1998	A
5823785	Matherne, Jr.	Oct 1998	A
5835077	Dao et al.	Nov 1998	A
5835277	Hegg	Nov 1998	A
5845053	Watanabe et al.	Dec 1998	A
5877777	Colwell	Mar 1999	A
5963891	Walker et al.	Oct 1999	A
6008470	Zhang et al.	Dec 1999	A
6037948	Liepa	Mar 2000	A
6049059	Kim	Apr 2000	A
6051805	Vaidya et al.	Apr 2000	A
6114645	Burgess	Sep 2000	A
6155475	Ekelof et al.	Dec 2000	A
6155928	Burdick	Dec 2000	A
6230327	Briand et al.	May 2001	B1
6236013	Delzenne	May 2001	B1
6236017	Smartt et al.	May 2001	B1
6242711	Cooper	Jun 2001	B1
6271500	Hirayama et al.	Aug 2001	B1
6330938	Herve et al.	Dec 2001	B1
6330966	Eissfeller	Dec 2001	B1
6331848	Stove et al.	Dec 2001	B1
D456428	Aronson, II et al.	Apr 2002	S
6373465	Jolly et al.	Apr 2002	B2
D456828	Aronson, II et al.	May 2002	S
D461383	Blackburn	Aug 2002	S
6441342	Hsu	Aug 2002	B1
6445964	White et al.	Sep 2002	B1
6492618	Flood et al.	Dec 2002	B1
6506997	Matsuyama	Jan 2003	B2
6552303	Blankenship et al.	Apr 2003	B1
6560029	Dobbie et al.	May 2003	B1
6563489	Latypov et al.	May 2003	B1
6568846	Cote et al.	May 2003	B1
D475726	Suga et al.	Jun 2003	S
6572379	Sears et al.	Jun 2003	B1
6583386	Ivkovich	Jun 2003	B1
6621049	Suzuki	Sep 2003	B2
6624388	Blankenship et al.	Sep 2003	B1
D482171	Vui et al.	Nov 2003	S
6647288	Madill et al.	Nov 2003	B2
6649858	Wakeman	Nov 2003	B2
6655645	Lu et al.	Dec 2003	B1
6660965	Simpson	Dec 2003	B2
6697701	Hillen et al.	Feb 2004	B2
6697770	Nagetgaal	Feb 2004	B1
6703585	Suzuki	Mar 2004	B2
6708385	Lemelson	Mar 2004	B1
6710298	Eriksson	Mar 2004	B2
6710299	Blankenship et al.	Mar 2004	B2
6715502	Rome et al.	Apr 2004	B1
D490347	Meyers	May 2004	S
6730875	Hsu	May 2004	B2
6734393	Friedl et al.	May 2004	B1
6744011	Hu et al.	Jun 2004	B1
6750428	Okamoto et al.	Jun 2004	B2
6765584	Wloka et al.	Jul 2004	B1
6768974	Nanjundan et al.	Jul 2004	B1
6772802	Few	Aug 2004	B2
6788442	Potin et al.	Sep 2004	B1
6795778	Dodge et al.	Sep 2004	B2
6798974	Nakano et al.	Sep 2004	B1
6857553	Hartman et al.	Feb 2005	B1
6858817	Blankenship et al.	Feb 2005	B2
6865926	O'Brien et al.	Mar 2005	B2
D504449	Butchko	Apr 2005	S
6920371	Hillen et al.	Jul 2005	B2
6940037	Kovacevic et al.	Sep 2005	B1
6940039	Blankenship et al.	Sep 2005	B2
7021937	Simpson et al.	Apr 2006	B2
7024342	Waite et al.	Apr 2006	B1
7126078	Demers et al.	Oct 2006	B2
7132617	Lee et al.	Nov 2006	B2
7170032	Flood	Jan 2007	B2
7194447	Harvey et al.	Mar 2007	B2
7247814	Ott	Jul 2007	B2
D555446	Picaza et al.	Nov 2007	S
7315241	Daily et al.	Jan 2008	B1
D561973	Kinsley et al.	Feb 2008	S
7353715	Myers	Apr 2008	B2
7363137	Brant et al.	Apr 2008	B2
7375304	Kainec et al.	May 2008	B2
7381923	Gordon et al.	Jun 2008	B2
7414595	Muffler	Aug 2008	B1
7465230	LeMay et al.	Dec 2008	B2
7478108	Townsend et al.	Jan 2009	B2
D587975	Aronson, II et al.	Mar 2009	S
7516022	Lee et al.	Apr 2009	B2
7580821	Schirm et al.	Aug 2009	B2
D602057	Osicki	Oct 2009	S
7621171	O'Brien	Nov 2009	B2
D606102	Bender et al.	Dec 2009	S
7643890	Hillen et al.	Jan 2010	B1
7687741	Kainec et al.	Mar 2010	B2
D614217	Peters et al.	Apr 2010	S
D615573	Peters et al.	May 2010	S
7817162	Bolick et al.	Oct 2010	B2
7853645	Brown et al.	Dec 2010	B2
D631074	Peters et al.	Jan 2011	S
7874921	Baszucki et al.	Jan 2011	B2
7970172	Hendrickson	Jun 2011	B1
7972129	O'Donoghue	Jul 2011	B2
7991587	Ihn	Aug 2011	B2
8069017	Hallquist	Nov 2011	B2
8224881	Spear et al.	Jul 2012	B1
8248324	Nangle	Aug 2012	B2
8265886	Bisiaux et al.	Sep 2012	B2
8274013	Wallace	Sep 2012	B2
8287522	Moses et al.	Oct 2012	B2
8316462	Becker et al.	Nov 2012	B2
8363048	Gering	Jan 2013	B2
8365603	Lesage et al.	Feb 2013	B2
8512043	Choquet	Aug 2013	B2
8569646	Daniel et al.	Oct 2013	B2
8680434	Stoger et al.	Mar 2014	B2
8747116	Zboray et al.	Jun 2014	B2
8777629	Kreindl et al.	Jul 2014	B2
8851896	Wallace et al.	Oct 2014	B2
RE45398	Wallace	Mar 2015	E
8992226	Leach et al.	Mar 2015	B1
9011154	Kindig et al.	Apr 2015	B2
9293056	Zboray et al.	Mar 2016	B2
9293057	Zboray et al.	Mar 2016	B2
9318026	Peters et al.	Apr 2016	B2
20010045808	Hietmann et al.	Nov 2001	A1
20010052893	Jolly et al.	Dec 2001	A1
20020032553	Simpson et al.	Mar 2002	A1
20020046999	Veikkolainen et al.	Apr 2002	A1
20020050984	Roberts	May 2002	A1
20020085843	Mann	Jul 2002	A1
20020175897	Pelosi	Nov 2002	A1
20030000931	Ueda et al.	Jan 2003	A1
20030025884	Hamana et al.	Feb 2003	A1
20030075534	Okamoto et al.	Apr 2003	A1
20030106787	Santilli	Jun 2003	A1
20030111451	Blankenship et al.	Jun 2003	A1
20030172032	Choquet	Sep 2003	A1
20030186199	McCool et al.	Oct 2003	A1
20030223592	Deruginsky et al.	Dec 2003	A1
20030234885	Pilu	Dec 2003	A1
20040020907	Zauner et al.	Feb 2004	A1
20040035990	Ackeret	Feb 2004	A1
20040050824	Samler	Mar 2004	A1
20040088071	Kouno et al.	May 2004	A1
20040140301	Blankenship et al.	Jul 2004	A1
20040181382	Hu et al.	Sep 2004	A1
20040217096	Lipnevicius	Nov 2004	A1
20050007504	Fergason	Jan 2005	A1
20050017152	Fergason	Jan 2005	A1
20050046584	Breed	Mar 2005	A1
20050050168	Wen et al.	Mar 2005	A1
20050101767	Clapham et al.	May 2005	A1
20050103766	Iizuka et al.	May 2005	A1
20050103767	Kainec et al.	May 2005	A1
20050109735	Flood	May 2005	A1
20050128186	Shahoian et al.	Jun 2005	A1
20050133488	Blankenship et al.	Jun 2005	A1
20050159840	Lin et al.	Jul 2005	A1
20050163364	Beck et al.	Jul 2005	A1
20050189336	Ku	Sep 2005	A1
20050199602	Kaddani et al.	Sep 2005	A1
20050230573	Ligertwood	Oct 2005	A1
20050252897	Hsu et al.	Nov 2005	A1
20050275913	Vesely et al.	Dec 2005	A1
20050275914	Vesely et al.	Dec 2005	A1
20060014130	Weinstein	Jan 2006	A1
20060076321	Maev et al.	Apr 2006	A1
20060136183	Choquet	Jun 2006	A1
20060142656	Malackowski et al.	Jun 2006	A1
20060154226	Maxfield	Jul 2006	A1
20060163227	Hillen et al.	Jul 2006	A1
20060166174	Rowe et al.	Jul 2006	A1
20060169682	Kainec et al.	Aug 2006	A1
20060173619	Brant et al.	Aug 2006	A1
20060189260	Sung	Aug 2006	A1
20060207980	Jacovetty et al.	Sep 2006	A1
20060213892	Ott	Sep 2006	A1
20060214924	Kawamoto et al.	Sep 2006	A1
20060226137	Huismann et al.	Oct 2006	A1
20060252543	Van Noland et al.	Nov 2006	A1
20060258447	Baszucki et al.	Nov 2006	A1
20070034611	Drius et al.	Feb 2007	A1
20070038400	Lee et al.	Feb 2007	A1
20070045488	Shin	Mar 2007	A1
20070088536	Ishikawa	Apr 2007	A1
20070112889	Cook et al.	May 2007	A1
20070198117	Wajihuddin	Aug 2007	A1
20070209586	Ebensberger	Sep 2007	A1
20070211026	Ohta	Sep 2007	A1
20070221797	Thompson et al.	Sep 2007	A1
20070256503	Wong et al.	Nov 2007	A1
20070277611	Portzgen et al.	Dec 2007	A1
20070291035	Vesely et al.	Dec 2007	A1
20080021311	Goldbach	Jan 2008	A1
20080031774	Magnant et al.	Feb 2008	A1
20080038702	Choquet	Feb 2008	A1
20080061113	Seki et al.	Mar 2008	A9
20080078811	Hillen et al.	Apr 2008	A1
20080078812	Peters et al.	Apr 2008	A1
20080117203	Gering	May 2008	A1
20080120075	Wloka	May 2008	A1
20080128398	Schneider	Jun 2008	A1
20080135533	Ertmer et al.	Jun 2008	A1
20080140815	Brant et al.	Jun 2008	A1
20080149686	Daniel et al.	Jun 2008	A1
20080203075	Feldhausen et al.	Aug 2008	A1
20080233550	Solomon	Sep 2008	A1
20080303197	Paquette et al.	Dec 2008	A1
20080314887	Stoger et al.	Dec 2008	A1
20090015585	Klusza	Jan 2009	A1
20090021514	Klusza	Jan 2009	A1
20090045183	Artelsmair et al.	Feb 2009	A1
20090057286	Ihara et al.	Mar 2009	A1
20090152251	Dantinne et al.	Jun 2009	A1
20090173726	Davidson et al.	Jul 2009	A1
20090184098	Daniel et al.	Jul 2009	A1
20090200281	Hampton	Aug 2009	A1
20090200282	Hampton	Aug 2009	A1
20090231423	Becker et al.	Sep 2009	A1
20090259444	Dolansky et al.	Oct 2009	A1
20090298024	Batzler	Dec 2009	A1
20090325699	Delgiannidis	Dec 2009	A1
20100012017	Miller	Jan 2010	A1
20100012637	Jaeger	Jan 2010	A1
20100048273	Wallace et al.	Feb 2010	A1
20100062405	Zboray	Mar 2010	A1
20100062406	Zboray	Mar 2010	A1
20100096373	Hillen et al.	Apr 2010	A1
20100121472	Babu et al.	May 2010	A1
20100133247	Mazumder et al.	Jun 2010	A1
20100133250	Sardy et al.	Jun 2010	A1
20100176107	Bong	Jul 2010	A1
20100201803	Melikian	Aug 2010	A1
20100224610	Wallace	Sep 2010	A1
20100276396	Cooper et al.	Nov 2010	A1
20100299101	Shimada et al.	Nov 2010	A1
20100307249	Lesage et al.	Dec 2010	A1
20110006047	Penrod	Jan 2011	A1
20110060568	Goldfine et al.	Mar 2011	A1
20110091846	Kreindl	Apr 2011	A1
20110114615	Daniel et al.	May 2011	A1
20110116076	Chantry et al.	May 2011	A1
20110117527	Conrardy et al.	May 2011	A1
20110122495	Togashi	May 2011	A1
20110183304	Wallace	Jul 2011	A1
20110187746	Suto et al.	Aug 2011	A1
20110248864	Becker et al.	Oct 2011	A1
20110290765	Albrecht et al.	Dec 2011	A1
20110316516	Schiefermuller et al.	Dec 2011	A1
20120122062	Yang et al.	May 2012	A1
20120189993	Kindig	Jul 2012	A1
20120291172	Wills et al.	Nov 2012	A1
20120298640	Conrardy et al.	Nov 2012	A1
20130026150	Chantry et al.	Jan 2013	A1
20130040270	Albrecht	Feb 2013	A1
20130049976	Maggiore	Feb 2013	A1
20130075380	Albrech et al.	Mar 2013	A1
20130182070	Peters et al.	Jul 2013	A1
20130183645	Wallace	Jul 2013	A1
20130189657	Wallace et al.	Jul 2013	A1
20130189658	Peters	Jul 2013	A1
20130209976	Postlethwaite et al.	Aug 2013	A1
20130230832	Peters	Sep 2013	A1
20130231980	Elgart et al.	Sep 2013	A1
20130327747	Dantinne et al.	Dec 2013	A1
20140017642	Postlethwaite et al.	Jan 2014	A1
20140038143	Daniel et al.	Feb 2014	A1
20140065584	Wallace et al.	Mar 2014	A1
20140134579	Becker	May 2014	A1
20140134580	Becker	May 2014	A1
20140263224	Becker	Sep 2014	A1
20140272835	Becker	Sep 2014	A1
20140272836	Becker	Sep 2014	A1
20140272837	Becker	Sep 2014	A1
20140272838	Becker	Sep 2014	A1
20140312020	Daniel	Oct 2014	A1
20140315167	Kreindl	Oct 2014	A1
20140322684	Wallace	Oct 2014	A1
20140346158	Matthews	Nov 2014	A1
20150056584	Boulware et al.	Feb 2015	A1
20150056585	Boulware et al.	Feb 2015	A1
20150056586	Penrod et al.	Feb 2015	A1

Foreign Referenced Citations (91)

Number	Date	Country
2698078	Sep 2011	CA
101209512	Jul 2008	CN
101214178	Jul 2008	CN
201083660	Jul 2008	CN
101419755	Apr 2009	CN
201229711	Apr 2009	CN
101571887	Nov 2009	CN
101587659	Nov 2009	CN
102014819	Apr 2011	CN
102165504	Aug 2011	CN
202684308	Jan 2013	CN
103871279	Jun 2014	CN
2833638	Feb 1980	DE
3046634	Jul 1982	DE
3244307	May 1984	DE
3522581	Jan 1987	DE
4037879	Jun 1991	DE
19615069	Oct 1997	DE
19739720	Oct 1998	DE
19834205	Feb 2000	DE
20009543	Aug 2001	DE
102005047204	Apr 2007	DE
102010038902	Feb 2012	DE
202012013151	Feb 2015	DE
0108599	May 1984	EP
0127299	Dec 1984	EP
0145891	Jun 1985	EP
0319623	Jun 1989	EP
0852986	Jul 1998	EP
1527852	May 2005	EP
1905533	Apr 2008	EP
2274736	May 2007	ES
1456780	Jul 1966	FR
2827066	Jan 2003	FR
2926660	Jul 2009	FR
1455972	Nov 1976	GB
1511608	May 1978	GB
2254172	Sep 1992	GB
2435838	Sep 2007	GB
2454232	May 2009	GB
2224877	Sep 1990	JP
5329645	Dec 1993	JP
07047471	Feb 1995	JP
07232270	Sep 1995	JP
8132274	May 1996	JP
8150476	Jun 1996	JP
H08505091	Jun 1996	JP
11104833	Apr 1999	JP
2000167666	Jun 2000	JP
2001071140	Mar 2001	JP
2002278670	Sep 2002	JP
2003200372	Jul 2003	JP
2003326362	Nov 2003	JP
2006006604	Jan 2006	JP
2006281270	Oct 2006	JP
2007290025	Nov 2007	JP
2009500178	Jan 2009	JP
2009160636	Jul 2009	JP
2012024867	Feb 2012	JP
20090010693	Jan 2009	KR
2008108601	Sep 2009	RU
1038963	Aug 1983	SU
WO-9845078	Oct 1998	WO
WO-0112376	Feb 2001	WO
WO-0143910	Jun 2001	WO
WO-0158400	Aug 2001	WO
WO-2005102230	Nov 2005	WO
WO-2006034571	Apr 2006	WO
WO-2007009131	Jan 2007	WO
WO-2007039278	Apr 2007	WO
WO-2009060231	May 2009	WO
WO-2009120921	Oct 2009	WO
WO-2009149740	Dec 2009	WO
WO-2010000003	Jan 2010	WO
WO-2010020867	Feb 2010	WO
WO-2010020870	Feb 2010	WO
WO-2010044982	Apr 2010	WO
WO-2010091493	Aug 2010	WO
WO-2011045654	Apr 2011	WO
WO-2011058433	May 2011	WO
WO-2011067447	Jun 2011	WO
WO-2011097035	Aug 2011	WO
WO-2012082105	Jun 2012	WO
WO-2012143327	Oct 2012	WO
WO-2013014202	Jan 2013	WO
WO-2013061518	May 2013	WO
WO-2013114189	Aug 2013	WO
WO-2013175079	Nov 2013	WO
WO-2014007830	Jan 2014	WO
WO-2014019045	Feb 2014	WO
WO-2014020386	Feb 2014	WO

Non-Patent Literature Citations (198)

Entry
“High Performance Computer Architectures—A Historical Perspective,” downloaded May 5, 2016.
http://homepages.inf.ed.ac.uk/cgi/rni/comparch. pl?Paru/perf.html,Paru/perf-f.html,Paru/menu-76.html.
Abbas, et al., Code Aster (Software) EDR (France) 14 pages, Oct. 2001.
Abbas, et al., Code—Aster; Introduction to Code—Aster; User Manual; Booket U1.0-: Introduction to Code—Aster; Document: U1.02.00; Version 7.4; Jul. 22, 2005.
Abida et al., “Numerical simulation to study the effect of tack welds and root gap on welding deformations and residual stresses of a pipe-flange joint”, Faculty of Mechanical Engineering, GIK Institute of Engineering Sciences and Technology, Topi, NWFP, Pakistan. Available online Aug. 25, 2005.
Adams, et al., “Adaptively sampled particle fluids,” ACM SIGGRAPH 2007 papers, Aug. 5-9, 2007, San Diego, California.
Agren, “Sensor Integration for Robotic Arc Welding;” 1995; vol. 5604C of Dissertations Abstracts International p. 1123; Dissertation Abs Online (Dialog® File 35): © 2012 ProQuest Info& Learning: http://dialogweb.com/cgi/dwclient?req=1331233317524; one (1) page; printed Mar. 8, 2012.
Aidun et al., Penetration in Spot GTA Welds during Centrifugation, Journal of Materials Engineering and Performance vol. 7(5) Oct. 1998—597.
Aidun, D., “Influence of Simulated High-g on the Weld Size of Al—Li Alloy” Elevator Science Ltd.; 2001; 4 pages.
Aiteanu et al., “Generation and Rendering of a Virtual Welding Seam in an Augmented Reality Training Environment” Proceedings of the Sixth IASTED International Conference, Aug. 2006, 8 pages.
Aiteanu, “Virtual and Augmented Reality Supervisor for a New Welding Helmet” Dissertation Nov. 15, 2005.
Aiteanu, et al., “A Step Forward in Manual Welding:; Demonstration of Augmented Reality Helmet” Institute of Automation, University of Bremen,; Germany, Proceedings of the Second IEEE and ACM International Symposium on Mixed and; Augmented Reality; 2003; 2 pages.
Aiteanu, et al., “Computer-Aided Manual Welding Using an Augmented; Reality Supervisor” Sheet Metal Welding Conference XII, Livonia, MI, May 9-12, 2006, 14 pages.
American Welding Society Advance Program of Programs and Events. Nov. 11-14, 2007. 31 pages. Chicago, IL.
American Welding Society Detroit Section, “Sheet Metal Welding Conference XII”, May 2006, 11 pages.
American Welding Society, “Vision for Welding Industry”; 41 pages.
American Welding Society, ANSI/A WS D 10.11 MID 10. 11 :2007 Guide for Root Pass Welding of Pipe without Backing Edition: 3rd American Welding Society/Oct. 13, 2006/36 pages ISBN: 0871716445.
American Welding Society, http://www.nsrp.org/6-presentations/WDVirtual—Welder. pdf (Virtual Reality Welder Training,; Project No. SI051, Navy ManTech Program, Project Review for Ship Tech 2005); 22 pages; Biloxi, MS.
American Welding Society, https://app.aws.org/conferences/defense/live index.html (AWS Welding in the Defense; Industry conference schedule, 2004); 12 pages.
American Welding Society, https://app.aws.org/w/r/www/wj/2005/03/WJ—2005—03.pdf (AWS Welding Journal, Mar. 2005; (see, e.g., p. 54)).; 114 pages.
American Welding Society, https://app.aws.org/wj/2004/04/052/njc (AWS Virtual Reality Program to Train Welders for; Shipbuilding, workshop information, 2004); 7 pages.
American Welding Society, https://app.aws.org/wj/2007 /11/WJ200711.pdf (AWS Welding Journal, Nov. 2007); 240 pages.
American Welding Society, Welding Handbook, Welding Science & Technology, Ninth Ed., Copyright 2001. Appendix A “Terms and Definitions”.
Antonelli et al, “A Semi-Automated Welding Station Exploiting Human-Robot Interaction,” Advanced Manufacturing Systems and Technology (2011) pp. 249-260.
ARC+—Archived Press Release from WayBack Machine from Jan. 31, 2008-Apr. 22, 2013, Page, https://web.archive.org/web/20121006041803/http://www.123certification.com/en/article—press/index.htm, Jan. 21, 2016, 3 pages.
ARC+ simulator; http://www.123arc.com/en/depliant—ang.pdf; 2000.
Arc+ Welding Simulation presentation; 25 pages.
ArcSentry Weld Quality Monitoring System; Native American Technologies, allegedly 2002, 5; pages.
ARS Electronica Linz Gmbh, Fronius, 2 pages, May 18, 1997.
Arvika Forum Vorstellung Projekt PAARi. BMW Group Virtual Reality Center. 4 pages.; Nuernberg. 2003.
Asciencetutor.com, A division of Advanced Science and Automation Corp., VWL (Virtual Welding Lab), 2 pages, 2007.
ASME Definitions, Consumables, Welding Positions, dated Mar. 19, 2001. See http://www.gowelding.com/wp/asme4.htm.
Balijepalli, et al. “Haptic Interfaces for Virtual Environment and Teleoperator Systems,” Haptics 2003, Department of Mechanical & Aerospace Engineering, State University of New York at Buffalo, NY.
Bargteil, et al., “A semi-lagrangian contouring method for fluid simulation,” ACM Transactions on Graphics, 25(1), 2006.
Bargteil, et al., “A texture synthesis method for liquid animations,” In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Sep. 2006.
Bender Shipbuilding and Repair Co. Virtual Welding—A Low Cost Virtual Reality Welding; Training System. Proposal submitted pursuant to MSRP Advanced Shipbuilding Enterprise; Research Announcement, Jan. 23, 2008. 28 pages, See also, http://www.nsrp.org/6-;Presentations/WD/020409 Virtual Welding Wilbur.pdf;;.
Borzecki, et al., Specialist Committee V.3 Fabrication Technology Committee Mandate, Aug. 20-25, 2006, 49 pages, vol. 2, 16th International Ship and Offshore Structures Congress, Southampton, UK.
Catalina, et al., “Interaction of Porosity with a Planar Solid/Liquid Interface” (“Catalina”), Metallurgical and Materials Transactions, vol. 35A, May 2004, pp. 1525-1538.
ChemWeb.com—Journal of Materials Engineering (printed Sep. 26, 2012) (01928041).
Chen, et al., “Self-Learning Fuzzy Neural Networks and Computer Vision for Control of Pulsed GTAW,” dated May 1997.
Chentanez, et al., “Liquid simulation on lattice-based tetrahedral meshes.” In ACM SIGGRAPH/Eurographics Symposium on Computer Animation 2007, pp. 219-228, Aug. 2007.
Chentanez, et al., “Simultaneous coupling of fluids and deformable bodies,” In ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 83-89, Aug. 2006.
Choquet, C., “ARC+: Today's Virtual Reality Solution for Welders” Internet Page, Jan. 1, 2008; 6 pages.
Choquet, C., “ARC+®: Today's Virtual Reality Solution for Welders”, Published in Proceedings of the IIW Internatioal Conference; Jul. 10-11, 2008; 19 pages.
Clausen, et al., “Simulating liquids and solid-liquid interactions with lagrangian meshes,” ACM Transactions on Graphics, 32(2):17:1-15, Apr. 2013. Presented at SIGGRAPH 2013.
Cooperative Research Program, Virtual Reality Welder Training, Summary Report SR 0512, 4 pages, Jul. 2005.
CS Wave, The Virtual Welding Trainer, 6 pages, 2007.
CS Wave-Manual, “Virtual Welding Workbench User Manual 3.0” 2007.
CUDA Programming Guide Version 1.1, Nov. 29, 2007.
Da Dalto, et al. “CS WAVE, A Virtual learning tool for welding motion”, 10 pages, Mar. 14, 2008.
Da Dalto, et al. “CS Wave: Learning welding motion in a virtual environment” Published in Proceedings of the IIW International Conference, Jul. 10-11, 2008.
Desroches, X.; Code-Aster, Note of use for aclculations of welding; Instruction manual U2.03 booklet: Thermomechanical; Document: U2.03.05; Oct. 1, 2003.
D'Huart, et al., “Virtual Environment for Training” 6th International Conference, ITS 20002, Jun. 2002; 6 pages.
Dotson, “Augmented Reality Welding Helmet Prototypes How Awesome the Technology Can Get,” Sep. 26, 2012, Retrieved from the Internet: URL: http://siliconangle.com/blog/2012/09/26/augmented-reality-welding-helmet-prototypes-how-awesome-the-technology-can-get/, retrieved on Sep. 26, 2014, 1 page.
Echtler et al, “17 The Intelligent Welding Gun: Augmented Reality for Experimental Vehicle Construction,” Virtual and Augmented Reality Applications in Manufacturing (2003) pp. 1-27.
Edison Welding Institute, E-Weld Predictor, 3 pages, 2008.
Eduwelding+, Training Activities with arc+ simulator; Weld Into the Future, Online Welding Simulator—A virtual training environment; 123arc.com; 6 pages, May 2008.
Eduwelding+, Weld Into the Future; Online Welding Seminar—A virtual training environment; 123arc.com; 4 pages, 2005.
Energetics, Inc. “Welding Technology Roadmap”, Sep. 2000, 38 pages.
Fast, K. et al., “Virtual Training for Welding”, Mixed and Augmented Reality, 2004, ISMAR 2004, Third IEEE and CM International Symposium on Arlington, VA, Nov. 2-5, 2004.
Feldman, et al., “Animating Suspended Particle Explosions”. In Proceedings of ACM SIGGRAPH 2003, pp. 708-715, Aug. 2003.
Feldman, et al., “Fluids in deforming meshes” In ACM SIGGRAPH/Eurographics Symposium on Computer Animation 2005, Jul. 2005.
Fite-Georgel, “Is there a Reality in Industrial Augmented Reality?” 10th IEEE International Symposium on Mixed and Augmented Reality (ISMAR). 10 pages, allegedly 2011.
Foster, et al., “Realistic animation of liquids,” Graphical Models and Image Processing, v.58 n.5, p. 471-483, Sep. 1996.
Foster, et al., “Practical animation of liquids,” Proceedings of the 28th annual conference on Computer graphics and interactive techniques, p. 23-30, Aug. 2001.
Garcia-Allende, et al., “Defect Detection in Arc-Welding Processes by Means of the Line-to-Continuum Method and Feature Selection” www.mdpi.com/journal/sensors; Sensors 2009, 9, 7753-7770: DOI; 10.3390/s91007753.
Goktekin, et al., “A Method for Animating Viscoelastic Fluids”. ACM Transactions on Graphics (Proc. of ACM SIGGRAPH 2004), 23(3):463-468, 2004.
Graham, “Texture Mapping” Carnegie Mellon University Class 15-462 Computer graphics, Lecture 10 dated Feb. 13, 2003; 53 pages.
Grahn, A., “Interactive Simulation of Contrast Fluid using Smoothed Particle Hydrodynamics,” Jan. 1, 2008, Master's Thesis in Computing Science, Umeå University, Department of Computing Science, Umeå Sweden.
Guu et al., “Technique for Simultaneous Real-Time Measurements of Weld Pool Surface Geometry and Arc Force,” Dec. 1992.
Heston, Virtually Welding—raining in a virtual environment gives welding students a leg up, retrieved on Apr. 12, 2010 from: http://www.thefabricator.com/article/arcwelding/virtually-welding.
Hillers, et al., “Augmented Reality—Helmet for the Manual; Welding Process” Institute of Automation, University of Bremen, Germany; 21 pages.
Hillers, et al., “Direct welding arc observation without harsh flicker,” 8 pages, allegedly FABTECH International and AWS welding show, 2007.
Hillers, et al., “Real time Arc-Welding Video Observation System.” 62nd International Conference of IIW, Jul. 12-17, 2009, 5 pages Singapore 2009.
Hillers, et al., “TEREBES:; Welding Helmet with AR Capabilities”, Institute of Automatic University Bremen; Institute of; Industrial Engineering and Ergonomics, 10 pages, alleqedlv 2004.
Hillis, et al., “Data Parallel Algorithms”, Communications of the ACM, Dec. 1986, vol. 29, No. 12, p. 1170.
Hirche, et al. “Hardware Accelerated Per-Pixel Displacement Mapping” University of Tubingen, Germany, Alexander Ehlert, Stefan Guthe, WStIGRfS & Michael Doggett, ATI Research; 8 pages.
Holmberg et al, “Efficient modeling and rendering of turbulent water over natural terrain,” In Proceedings of the 2nd international conference on Computer graphics and interactive techniques in Australasia and South East Asia (GRAPHITE '04) 2004.
http://www.sciencedirect.com/science/article/pil/S009457650000151X.
Hu et al. “Heat and mass transfer in gas metal arc welding. Part 1: the arc” found in ScienceDirect, International Journal of Heat and Mass transfer 50 (2007) 833-846 Available on Line on Oct. 24, 2006 http://web.mst.edu/˜tsai/publications/Hu-IJHMT-2007-1-60.pdf.
Impact Welding: examples from current and archived website, trade shows, etc. See, e.g.,; http://www.impactweldinq.com. 53 pages.
Irving, et al., “Efficient simulation of large bodies of water by coupling two and three dimensional techniques,” ACM SIGGRAPH 2006 Papers, Jul. 30-Aug. 3, 2006, Boston, Massachusetts.
Jeffus, “Welding Principles and Applications” Sixth Edition, 2008, 10 pages.
Jonsson et al. “Simulation of Tack Welding Procedures in Butt Joint Welding of Plates” Research Supplement, Oct. 1985.
Juan Vicenete Rosell Gonzales, “RV-Sold: simulator virtual para la formacion de soldadores”; Deformacion Metalica, Es. vol. 34, No. 301 Jan. 1, 2008.
Kass, et al., “Rapid, Stable Fluid Dynamics for Computer Graphics,” Proceedings of SIGGRAPH '90, in Computer Graphics, vol. 24, No. 4, pp. 49-57, 1990.
Klingner, et al., “Fluid animation with dynamic meshes,” In Proceedings of ACM SIGGRAPH 2006, pp. 820-825, Aug. 2006.
Kobayashi, et al., “Simulator of Manual Metal Arc Welding with Haptic Display” (“Kobayashi 2001”), Proc. of the 11th International Conf. on Artificial Reality and Telexistence (ICAT), Dec. 5-7, 2001, pp. 175-178, Tokyo, Japan.
Kobayashi, et al., “Skill Training System of Manual Arc Welding by Means of Face-Shield-Like HMD and Virtual Electrode” (“Kobayashi 2003”), Entertainment Computing, vol. 112 of the International Federation for Information Processing (IFIP), Springer Science + Business Media, New York, copyright 2003, pp. 389-396.
Lincoln Global, Inc., “VRTEX 360: Virtual Reality Arc Welding Trainer” Brochure (2015) 4 pages.
Lindholm, et al., “NVIDIA Testla: A Unifired Graphics and Computing Architecture”, IEEE Computer Society, 2008.
Mahrle, A., et al.; “the influence of fluid flow phenomena on the laser beam welding process” International Journal of Heat and Fluid Flow 23 (2002, No. 3, pp. 288-297; Institute of Fluid Dynamics and Thermodynamics, Otto-von-Guericke University Magdeburg, P.O. Box 4120, D-39016 Magdeburg, Germany.
Mann, et al., “Realtime HDR (High Dynamic Range) Video for Eyetap Wearable Computers, FPGA-Based Seeing Aids, and Glasseyes (EYETAPS),” 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE),pp. 1-6, Apr. 29, 2012, 6 pages.
Mantinband, et al., “Autosteroscopic, field-sequential display with full freedom of movement or Let the display were the shutter-glasses,” 3ality (Israel) Ltd., 2002.
Mavrikios D et al, A prototype virtual reality-based demonstrator for immersive and interactive simulation of welding processes, International Journal of Computer Integrated manufacturing, Taylor and Francis, Basingstoke, GB, vol. 19, No. 3, Apr. 1, 2006, pp. 294-300.
Miller Electric Mfg. Co, “LiveArc: Welding Performance Management System” Owner's Manual, (Jul. 2014) 64 pages.
Miller Electric Mfg. Co., “LiveArc Welding Performance Management System” Brochure, (Dec. 2014) 4 pages.
Miller Electric Mfg. Co.; MIG Welding System features weld monitoring software; NewsRoom 2010 (Dialog® File 992); © 2011 Dialog. 2010; http://www.dialogweb.com/cgi/dwclient?reg=1331233430487; three (3) pages; printed Mar. 8, 2012.
Moore, “No exponential is forever: but ‘Forever’ can be delayed!,” IEEE International Solid-State Circuits Conference, 2003.
Müller, et al., “Particle-based fluid simulation for interactive applications,” Proceedings of the 2003 ACM SIGGRAPH/Eurographics symposium on Computer animation, Jul. 26-27, 2003, San Diego, California.
Müller, et al., “Point Based Animation of Elastic, Plastic and Melting Objects,” Eurographics/ACM SIGGRAPH Symposium on Computer Animation (2004).
N. A. Tech., P/NA.3 Process Modeling and Optimization, 11 pages, Jun. 4, 2008.
Nasios, “Improving Chemical Plant Safety Training Using Virtual Reality,” Thesis submitted to the University of Nottingham for the Degree of Doctor of Philosophy, Dec. 2001.
Nealen, A., “Point-Based Animation of Elastic, Plastic, and Melting Objects,” CG topics, Feb. 2005.
Nordruch, et al., “Visual Online Monitoring of PGMAW Without a Lighting Unit”, Jan. 2005.
NSRP ASE, Low-Cost Virtual Realtiy Welder Training System, 1 Page, 2008.
O'Brien et al., “Dynamic Simulation of Splashing Fluids”. In Proceedings of Computer Animation 95, pp. 198-205, Apr. 1995.
O'Brien, “Google's Project Glass gets some more details”, Jun. 27, 2012 (Jun. 27, 2012), Retrieved from the Internet: http://www.engadget.com/2012/06/27/googles-project-glass-gets-some-more-details/, retrieved on Sep. 26, 2014, 1 page.
P/NA.3 Process Modelling and Optimization; Native American Technologies, allegedly 2002,; 5 paqes.
Penrod, “New Welder Training Tools.” EWI PowerPoint presentation; 16 pages allegedly 2008.
Phar, “GPU Gems 2 Programming Techniques for High-Performance Graphics and General-Purpose Computation,” 2005, 12 pages.
Porter, et al. Virtual Reality Welder Trainer, Session 5: Joining Technologies for Naval Applications: earliest date Jul. 14, 2006 (http://weayback.archive.org) Edision Welding Institute: J. Allan Cote, General Dynamics Electric Boat; Timothy D. Gifford, VRSim, and Wim Lam, FCS Controls.
Porter, et al., Virtual Reality Training, Paper No. 2005-P19, 14 pages, 2005.
Porter, et al., Virtual Reality Training, vol. 22, No. 3, Aug. 2006; 13 pages.
Porter, et al., Virtual Reality Welder Training, dated Jul. 14, 2006.
Praxair Technology Inc., “The RealWeld Trainer System: Real Weld Training Under Real Conditions” Brochure (2013) 2 pages.
Premoze, et al., “Particle-based simulation of fluids,” Comput. Graph. Forum 22, 3, 401-410, 2003.
Rasmussen, et al., “Directable photorealistic liquids,” Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation, Aug. 27-29, 2004, Grenoble, France.
Ratnam, et al., “Automatic classification of weld defects using simulated data and an MLP neutral network.” Insight vol. 49, No. 3; Mar. 2007.
Reeves, “Particles Systems—A Technique for Modeling a Class of Fuzzy Objects”, Computer Graphics 17:3 pp. 359-3761983.
Renwick, et al., “Experimental Investigation of GTA Weld Pool Oscillations” Welding Research—Supplement to the Welding Journal, Feb. 1983, 7 pages.
Rodjito, “Position tracking and motion prediction using Fuzzy Logic,” 2006, Colby College.
Russel, et al., “Artificial Intelligence: A Modern Approach”, Prentice-Hall (Copywrite 1995).
Sandor, et al., “Lessons Learned in Designing Ubiquitous Augmented; Reality User Interfaces.” 21 pages, allegedly from Emerging Technologies of Augmented; Reality: Interfaces Eds. Haller, M.; Billinghurst, M.; Thomas, B. Idea Group Inc. 2006.
Sandor, et al., “PAARTI: Development of an Intelligent Welding Gun for; BMW.” PIA2003, 7 pages, Tokyo. 2003.
Sandter, et al. Fronius—virtual welding, FH Joanne Um, Gesellschaft mbH, University of; Annlied Sciences 2 pages, May 12, 2008.
Schoder, “Design and Implementation of a Video Sensor for Closed Loop Control of Back Bead Weld Puddle Width,” Massachusetts Institute of Technology, Dept. of Mechanical Engineering, May 27, 1983.
Screen Shot of CS Wave Control Centre V3.0.0 https://web.archive.org/web/20081128081915/http:/wave.c-s.fr/images/english/snap—evolution4.jpg.
Screen Shot of CS Wave Control Centre V3.0.0 https://web.archive.org/web/20081128081817/http:/wave.c-s.fr/images/english/snap—evolution6.jpg.
Screen Shot of CS Wave Exercise 135.FWPG Root Pass Level 1 https://web.archive.org/web/20081128081858/http:/wave.c-s.fr/images/english/snap—evolution2.jpg.
Sim Welder, retrieved on Apr. 12, 2010 from: http://www.simwelder.com.
SIMFOR/CESOL, “RV-Sold” Welding Simulator, Technical and Functional Features, 20 pages, estimated Jan. 2010.
Slater, et al., “Mechanisms and Mechanical Devices Sourcebook,” McGraw Hill; 2nd Addition, 1996.
Stam, J., “Stable fluids,” Proceedings of the 26th annual conference on Computer graphics and interactive techniques, p. 121-128, Jul. 1999.
SWANTEC corporate web page downloaded Apr. 19, 2016. http://www.swantec.com/technology/numerical-simulation/.
Tamasi, T., “The Evolution of Computer Graphics,” NVIDIA, 2008.
Teeravarunyou, et al, “Computer Based Welding Training System,” International Journal of Industrial Engineering (2009) 16(2): 116-125.
Terebes: examples from http://www.terebes.uni-bremen.de.; 6 pages.
The Fabricator, Virtual Welding, 4 pages, Mar. 2008.
The Lincoln Electric Company, “VRTEX Virtual Reality Arc Welding Trainer,” http://www.lincolnelectric.com/en-us/equipment/training-equipment/Pages/vrtex.aspx as accessed on Jul. 10, 2015, 3 pages.
The Lincoln Electric Company, Production Monitoring 2 brochure, 4 pages, May 2009.
The Lincoln Electric Company; CheckPoint Production Monitoring borchure; four (4) pages; http://www.lincolnelectric.com/assets/en—US/products/literature/s232.pdf; Publication S2.32; Issue Date Feb. 2012.
Thurey, et al., “Real-time BreakingWaves for Shallow Water Simulations,” In Proceedings of the 15th Pacific Conference on Computer Graphics and Applications (PG '07) 2007.
Tonnesen, D., “Modeling Liquids and Solids using Thermal Particles,” Proceedings of Graphics Interface'91, pp. 255-262, Calgary, Alberta, 1991.
Tschirner, et al., “Virtual and Augmented Reality for Quality Improvement of Manual Welds” National Institute of Standards and Technology, Jan. 2002, Publication 973, 24 pages.
Tschirner, et al, “A Concept for the Application of Augmented Reality in Manual Gas Metal Arc Welding.” Proceedings of the International Symposium on Mixed and Augmented Reality; 2 pages; 2002.
Vesterlund, M., “Simulation and Rendering of a Viscous Fluid using Smoothed Particle Hydrodynamics,” Dec. 3, 2004, Master's Thesis in Computing Science, Umeå University, Department of Computing Science, Umeå, Sweden.
Viega, et al. “Simulation of a Work Cell in the IGRIP Program” dated 2006: 50 pages.
Virtual Welding: A Low Cost Virtual Reality Welder Training System, NSRP RA 07-01—BRP Oral Review Meeting in Charleston, SC at ATI, Mar. 2008.
ViziTech USA, “Changing the Way America Learns,” http://vizitechusa.com/ accessed on Mar. 27, 2014; 2 pages.
VRSim Inc. “About Us—History” www.vrsim.net/history, 2016, 1 page.
VRSim Powering Virtual Reality, www.lincolnelectric.com/en-us/equipmenl/Iraining-equipmenl/Pages/powered-by-; 'rsim.aspx, 2016, 1 page.
Wade, “Human uses of ultrasound: ancient and modern”, Ultrasonics vol. 38, dated 2000.
Wahi, et al., “Finite-Difference Simulation of a Multi-Pass Pipe Weld” (“Wahi”), vol. L, paper 3/1, International Conference on Structural Mechanics in Reactor Technology, San Francisco, CA, Aug. 15-19, 1977.
Wang, et al. “Numerical Analysis of Metal Tranfser in Gas Metal Arc Welding, ” Departements of Mechanical and Electrical Engineering. University of Kentucky, Dec. 10, 2001.
Wang, et al., “Impingement of Filler Droplets and Weld Pool During Gas Metal Arc Welding Process” International Journal of Heat and Mass Transfer, Sep. 1999, 14 pages.
Wang, et al., “Study on welder training by means of haptic guidance and virtual reality for arc welding,” 2006 IEEE International Conference on Robotics and Biomimetics, ROBIO 2006 ISBN-10: 1424405718, p. 954-958.
Webster's II new college dictionary, 3rd ed., Houghton Mifflin Co., copyright 2005, Boston, MA, p. 1271, definition of “wake.”.
White, et al., Virtual welder training, 2009 IEEE Virtual Reality Conference, p. 303, 2009.
Wu, “Microcomputer-based welder training simulator”, Computers in Industry, vol. 20, No. 3, Oct. 1992, pp. 321-325, XP000205597, Elsevier Science Publishers, Amsterdam, NL.
Wuhan Onew Technology Co Ltd, “Onew-360 Welding Training Simulator” http://en.onewtech.com/—d276479751.htm as accessed on Jul. 10, 2015, 12 pages.
Yao, et al., “Development of a Robot System for Pipe Welding” 2010 International Conference on Measuring Technology and Mechatronics Automation. Retrieved from the Internet: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5460347&tag=1; pp. 1109-1112.
Yoder, Fletcher, Opinion Re. 45,398 and U.S. Appl. No. 14/589,317, including Appendices; Sep. 9, 2015; 1700 pages.
United States Provisional Patent Application for “System for Characterizing Manual Welding Operations on Pipe and Other Curved Structures,” U.S. Appl. No. 62/055,724, filed Sep. 26, 2014, 35 pages.
Office Action from U.S. Appl. No. 14/526,914 dated Feb. 3, 2017.
International Search Report for PCT/IB2009/00605.
International Search Report and Written Opinion from PCT/IB10/02913 dated Apr. 19, 2011.
International Search Report for PCT/IB2014/001796, dated Mar. 24, 3016; 8 pages.
International Search Report for PCT/IB2015/000161, dated Aug. 25, 2016; 9 pages.
International Search Report for PCT/IB2015/000777, dated Dec. 15, 2016; 11 pages.
International Search Report for PCT/IB2015/000814 dated Dec. 15, 2016; 9 pages.
International Preliminary Report from PCT/IB2015/001084 dated Jan. 26, 2017.
Petition for Inter Partes Review of U.S. Pat. No. 8,747,116; IPR 2016-00749; Apr. 7, 2016; 70 pages.
Declaration of Edward Bohnart, Apr. 27, 2016, exhibit to IPR 2016-00749.
Declaration of Dr. Michael Zyda, May 3, 2016, exhibit to IPR 2016-00749.
Trial Denied IPR Proceeding of U.S. Pat. No. 8,747,116; IPR 2016-00749; Sep. 21, 2016: 21 pages.
Petition for Inter Partes Review of U.S. Pat. No. Re. 45,398; IPR 2016-00840; Apr. 18, 2016; 71 pages.
Declaration of AxelGraeser, Apr. 17, 2016, exhibit to IPR 2016-00840; 88 pages.
Decision Denying Request for Rehearing of U.S. Pat. No. Re. 45,398; IPR 2016-00840; Nov. 17, 2016; 10 pages.
Petition for Inter Partes Review of U.S. Pat. No. 8,747,116; IPR 2016-01568; Aug. 9, 2016; 75 pages.
Decision Termination Proceeding of U.S. Pat. No. 8,747,116; IPR 2016-01568; Nov. 15, 2016; 4 pages.
Petition for Inter Partes Review of U.S. Pat. No. 9,293,056; IPR 2016-00904; May 9, 2016; 91 pages.
Declaration of Edward Bohnart, Apr. 27, 2016, exhibit to IPR 2016-00904; 22 pages.
Declaration of Dr. Michael Zyda, May 3, 2016, exhibit to IPR 2016-00904; 76 pages.
Decision Trial Denied IPR Proceeding of U.S. Pat. No. 9,293,056; IPR 2016-00904; Nov. 3, 2016; 15 pages.
Petition for Inter Partes Review of U.S. Pat. No. 9,293,057; IPR 2016-00905; May 9, 2016; 87 pages.
Declaration of Edward Bohnart, Apr. 27, 2016, exhibit to IPR 2016-00905; 23 pages.
Declaration of Dr. Michael Zyda, May 3, 2016, exhibit to IPR 2016-00905; 72 pages.
Decision Trial Denied IPR Proceeding of U.S. Pat. No. 9,293,057; IPR 2016-00905; Nov. 3, 2016; 21 pages.
Lincoln Electric Company et al v. Seabery Soluciones SL et al—1:15-cv-01575-DCN—Complaint filed Aug. 15, 2015.
Lincoln Electric Company et al v. Seabery Soluciones SL et al—1:15-cv-01575-DCN—Amended Answer filed Mar. 1, 2016 by Seabery North America.
Lincoln Electric Company et al v. Seabery Soluciones SL et al—1:15-cv-01575-DCN—Amended Answer filed Mar. 1, 2016 by Seabery Soluciones SL.
Lincoln Electric Company et al v. Seabery Soluciones SL et al—1:15-cv-01575-DCN—Amended Answer filed Mar. 22, 2016 by Lincoln Electri c Company.
Lincoln Electric Company et al v. Seabery Soluciones SL et al—1:15-cv-01575-DCN—Answer filed Mar. 22, 2016 by Lincoln Global Inc.
Exhibit B from Declaration of Morgan Lincoln in Lincoln Electric Co. et al. v. Seabery Soluciones, S.L. et al., Case No. 1:15-cv-01575-DCN, dated Dec. 20, 2016, 5 pages.
Sun Yaoming; Application of Micro Computer in Robotic Technologies; Science and Technology Literature Press; Catalogue of New Books of Science and Technology; Sep. 1987, pp. 360-363.
Kenneth Fast; Virtual Welding—A Low Cost Virtual Reality Welder system training system phase II; NSRP ASE Technology Investment Agreement; Feb. 29, 2012; pp. 1-54.
Arc Simulation & Certification, Weld Into the Future, 4 pages, 2005.

Related Publications (1)

	Number	Date	Country
	20170178537 A1	Jun 2017	US

Provisional Applications (1)

	Number	Date	Country
	61090794	Aug 2008	US

Continuations (2)

	Number	Date	Country
Parent	14821051	Aug 2015	US
Child	15453787		US
Parent	13792309	Mar 2013	US
Child	14821051		US

Continuation in Parts (1)

	Number	Date	Country
Parent	12501257	Jul 2009	US
Child	13792309		US

Importing and analyzing external data using a virtual reality welding system

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