The present disclosure relates generally to a system and method for simulation and additive manufacturing. In one embodiment, stress in additively manufactured parts is simulated and parameters adjusted until design tolerances are met.
Traditional component machining often relies on removal of material by drilling, cutting, or grinding to form a part. In contrast, additive manufacturing, also referred to as 3D printing, typically involves sequential layer by layer addition of material to build a part. Beginning with a 3D computer model, an additive manufacturing system can be used to create complex parts from a wide variety of materials.
The powder bed fusion (PBF) technique for additive manufacturing of metals, ceramics, and plastics is well suited for manufacture of a wide range of parts. However, computationally modeling powder bed fusion parts is difficult. For example, thermal expansion and the buildup of internal stresses are difficult to computationally model in complex parts, but ignoring or failing to compensate for these issues can result in weak or failed parts.
Accurate modeling can involve capturing the effects of surface tension, photon absorption, emission, reflection, transmission, scattering, thermal conduction in the gas/powder particles/previous layer(s), advection of the gas, thermal expansion of the powder particles and the previous layer(s), phase change including melting, gasification, condensation, and solidification, and in cases of high temperature melting materials (such as metals/ceramics), thermal radiation heat transfer. Other physics such as gas photon absorption, buoyancy, and if ceramics are to be modeled accurately, chemistry needs to be involved.
Unfortunately, such physics modeling is generally too difficult for currently available explicit solution solvers, due in part to the exceedingly large number of interconnected variables that need to be solved implicitly. While such implicit solution methods can use various numerical analysis techniques, to solve the modeling problem a full multi-physics model is typically required. For example, Finite Element Analysis (FEA) can be used for static structural problems, and Computational Fluid Dynamics (CFD) for fluid flow. However, to accurately solve for the resultant stress distributions and geometry after an additively manufactured part has cooled, the thermal history of the part is required. Thermal history calculations require a full suite of physics modeling, which is not captured by conventional FEA and CFD analyses. Commonly available commercial modeling packages either lack the physics capabilities to model the additive manufacturing process in sufficient detail, or use of the required number of additional physics packages causes the solution time to grow to timescales of a month or more at which point it is of limited industrial use.
If the additive manufacturing process could be modeled in sufficient detail and fast enough for part prototyping and manufacture, appropriate accuracy on the alterations to key processing parameters could be made such that the “As-Printed” part equals the “As Desired” part.
Non-limiting and non-exhaustive embodiments of the present disclosure are described with reference to the following figures, wherein like reference numerals refer to like parts throughout the various figures unless otherwise specified.
In the following description, reference is made to the accompanying drawings that form a part thereof, and in which is shown by way of illustrating specific exemplary embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the concepts disclosed herein, and it is to be understood that modifications to the various disclosed embodiments may be made, and other embodiments may be utilized, without departing from the scope of the present disclosure. The following detailed description is, therefore, not to be taken in a limiting sense.
An additive manufacturing system which has one or more energy sources, including in one embodiment, one or more laser or electron beams, are positioned to emit one or more energy beams. Beam shaping optics may receive the one or more energy beams from the energy source and form a single beam. An energy patterning unit receives or generates the single beam and transfers a two-dimensional pattern to the beam, and may reject the unused energy not in the pattern. An image relay receives the two-dimensional patterned beam and focuses it as a two-dimensional image to a desired location on a height fixed or movable build platform (e.g. a powder bed). In certain embodiments, some or all of any rejected energy from the energy patterning unit is reused.
In some embodiments, multiple beams from the laser array(s) are combined using a beam homogenizer. This combined beam can be directed at an energy patterning unit that includes either a transmissive or reflective pixel addressable light valve. In one embodiment, the pixel addressable light valve includes both a liquid crystal module having a polarizing element and a light projection unit providing a two-dimensional input pattern. The two-dimensional image focused by the image relay can be sequentially directed toward multiple locations on a powder bed to build a 3D structure.
As seen in
Energy source 112 generates photon (light), electron, ion, or other suitable energy beams or fluxes capable of being directed, shaped, and patterned. Multiple energy sources can be used in combination. The energy source 112 can include lasers, incandescent light, concentrated solar, other light sources, electron beams, or ion beams. Possible laser types include, but are not limited to: Gas Lasers, Chemical Lasers, Dye Lasers, Metal Vapor Lasers, Solid State Lasers (e.g. fiber), Semiconductor (e.g. diode) Lasers, Free electron laser, Gas dynamic laser, “Nickel-like” Samarium laser, Raman laser, or Nuclear pumped laser.
A Gas Laser can include lasers such as a Helium-neon laser, Argon laser, Krypton laser, Xenon ion laser, Nitrogen laser, Carbon dioxide laser, Carbon monoxide laser or Excimer laser.
A Chemical laser can include lasers such as a Hydrogen fluoride laser, Deuterium fluoride laser, COIL (Chemical oxygen-iodine laser), or Agil (All gas-phase iodine laser).
A Metal Vapor Laser can include lasers such as a Helium-cadmium (HeCd) metal-vapor laser, Helium-mercury (HeHg) metal-vapor laser, Helium-selenium (HeSe) metal-vapor laser, Helium-silver (HeAg) metal-vapor laser, Strontium Vapor Laser, Neon-copper (NeCu) metal-vapor laser, Copper vapor laser, Gold vapor laser, or Manganese (Mn/MnCl2) vapor laser.
A Solid State Laser can include lasers such as a Ruby laser, Nd:YAG laser, NdCrYAG laser, Er:YAG laser, Neodymium YLF (Nd:YLF) solid-state laser, Neodymium doped Yttrium orthovanadate (Nd:YVO4) laser, Neodymium doped yttrium calcium oxoborateNd:YCa4O(BO3)3 or simply Nd:YCOB, Neodymium glass (Nd:Glass) laser, Titanium sapphire (Ti:sapphire) laser, Thulium YAG (Tm:YAG) laser, Ytterbium YAG (Yb:YAG) laser, Ytterbium:2O3 (glass or ceramics) laser, Ytterbium doped glass laser (rod, plate/chip, and fiber), Holmium YAG (Ho:YAG) laser, Chromium ZnSe (Cr:ZnSe) laser, Cerium doped lithium strontium (or calcium)aluminum fluoride (Ce:LiSAF, Ce:LiCAF), Promethium 147 doped phosphate glass (147Pm+3:Glass) solid-state laser, Chromium doped chrysoberyl (alexandrite) laser, Erbium doped anderbium-ytterbium co-doped glass lasers, Trivalent uranium doped calcium fluoride (U:CaF2) solid-state laser, Divalent samarium doped calcium fluoride (Sm:CaF2) laser, or F-Center laser.
A Semiconductor Laser can include laser medium types such as GaN, InGaN, AlGaInP, AlGaAs, InGaAsP, GaInP, InGaAs, InGaAsO, GaInAsSb, lead salt, Vertical cavity surface emitting laser (VCSEL), Quantum cascade laser, Hybrid silicon laser, or combinations thereof.
For example, in one embodiment a single Nd:YAG q-switched laser can be used in conjunction with multiple semiconductor lasers. In another embodiment, an electron beam can be used in conjunction with an ultraviolet semiconductor laser array. In still other embodiments, a two-dimensional array of lasers can be used. In some embodiments with multiple energy sources, pre-patterning of an energy beam can be done by selectively activating and deactivating energy sources.
Beam shaping unit 114 can include a great variety of imaging optics to combine, focus, diverge, reflect, refract, homogenize, adjust intensity, adjust frequency, or otherwise shape and direct one or more energy beams received from the energy source 112 toward the energy patterning unit 116. In one embodiment, multiple light beams, each having a distinct light wavelength, can be combined using wavelength selective mirrors (e.g. dichroics) or diffractive elements. In other embodiments, multiple beams can be homogenized or combined using multifaceted mirrors, microlenses, and refractive or diffractive optical elements.
Energy patterning unit 116 can include static or dynamic energy patterning elements. For example, photon, electron, or ion beams can be blocked by masks with fixed or movable elements. To increase flexibility and ease of image patterning, pixel addressable masking, image generation, or transmission can be used. In some embodiments, the energy patterning unit includes addressable light valves, alone or in conjunction with other patterning mechanisms to provide patterning. The light valves can be transmissive, reflective, or use a combination of transmissive and reflective elements. Patterns can be dynamically modified using electrical or optical addressing. In one embodiment, a transmissive optically addressed light valve acts to rotate polarization of light passing through the valve, with optically addressed pixels forming patterns defined by a light projection source. In another embodiment, a reflective optically addressed light valve includes a write beam for modifying polarization of a read beam. In yet another embodiment, an electron patterning device receives an address pattern from an electrical or photon stimulation source and generates a patterned emission of electrons.
Rejected energy handling unit 118 is used to disperse, redirect, or utilize energy not patterned and passed through the energy pattern image relay 120. In one embodiment, the rejected energy handling unit 118 can include passive or active cooling elements that remove heat from the energy patterning unit 116. In other embodiments, the rejected energy handling unit can include a “beam dump” to absorb and convert to heat any beam energy not used in defining the energy pattern. In still other embodiments, rejected beam energy can be recycled using beam shaping optics 114. Alternatively, or in addition, rejected beam energy can be directed to the article processing unit 140 for heating or further patterning. In certain embodiments, rejected beam energy can be directed to additional energy patterning systems or article processing units.
Image relay 120 receives a patterned image (typically two-dimensional) from the energy patterning unit 116 and guides it toward the article processing unit 140. In a manner similar to beam shaping optics 114, the image relay 120 can include optics to combine, focus, diverge, reflect, refract, adjust intensity, adjust frequency, or otherwise shape and direct the patterned image.
Article processing unit 140 can include a walled chamber 148 and bed 144, and a material dispenser 142 for distributing material. The material dispenser 142 can distribute, remove, mix, provide gradations or changes in material type or particle size, or adjust layer thickness of material. The material can include metal, ceramic, glass, polymeric powders, other melt-able material capable of undergoing a thermally induced phase change from solid to liquid and back again, or combinations thereof. The material can further include composites of melt-able material and non-melt-able material where either or both components can be selectively targeted by the imaging relay system to melt the component that is melt-able, while either leaving along the non-melt-able material or causing it to undergo a vaporizing/destroying/combusting or otherwise destructive process. In certain embodiments, slurries, sprays, coatings, wires, strips, or sheets of materials can be used. Unwanted material can be removed for disposable or recycling by use of blowers, vacuum systems, sweeping, vibrating, shaking, tipping, or inversion of the bed 146.
In addition to material handling components, the article processing unit 140 can include components for holding and supporting 3D structures, mechanisms for heating or cooling the chamber, auxiliary or supporting optics, and sensors and control mechanisms for monitoring or adjusting material or environmental conditions. The article processing unit can, in whole or in part, support a vacuum or inert gas atmosphere to reduce unwanted chemical interactions as well as to mitigate the risks of fire or explosion (especially with reactive metals).
Control processor 150 can be connected to control any components of additive manufacturing system 100. The control processor 150 can be connected to variety of sensors, actuators, heating or cooling systems, monitors, and controllers to coordinate operation. A wide range of sensors, including imagers, light intensity monitors, thermal, pressure, or gas sensors can be used to provide information used in control or monitoring. The control processor can be a single central controller, or alternatively, can include one or more independent control systems. The controller processor 150 is provided with an interface to allow input of manufacturing instructions. Use of a wide range of sensors allows various feedback control mechanisms that improve quality, manufacturing throughput, and energy efficiency.
In step 204, unpatterned energy is emitted by one or more energy emitters, including but not limited to solid state or semiconductor lasers, or electrical power supply flowing electrons down a wire. In step 206, the unpatterned energy is shaped and modified (e.g. intensity modulated or focused). In step 208, this unpatterned energy is patterned, with energy not forming a part of the pattern being handled in step 210 (this can include conversion to waste heat, or recycling as patterned or unpatterned energy). In step 212, the patterned energy, now forming a two-dimensional image is relayed toward the material. In step 214, the image is applied to the material, building a portion of a 3D structure. These steps can be repeated (loop 218) until the image (or different and subsequent image) has been applied to all necessary regions of a top layer of the material. When application of energy to the top layer of the material is finished, a new layer can be applied (loop 216) to continue building the 3D structure. These process loops are continued until the 3D structure is complete, when remaining excess material can be removed or recycled.
The optically addressed light valve 380 is stimulated by the light (typically ranging from 400-500 nm) and imprints a polarization rotation pattern in transmitted beam 313 which is incident upon polarizer 382. The polarizer 382 splits the two polarization states, transmitting p-polarization into beam 317 and reflecting s-polarization into beam 315 which is then sent to a beam dump 318 that handles the rejected energy. As will be understood, in other embodiments the polarization could be reversed, with s-polarization formed into beam 317 and reflecting p-polarization into beam 315. Beam 317 enters the final imaging assembly 320 which includes optics 384 that resize the patterned light. This beam reflects off of a movable mirror 386 to beam 319, which terminates in a focused image applied to material bed 344 in an article processing unit 340. The depth of field in the image selected to span multiple layers, providing optimum focus in the range of a few layers of error or offset.
The bed 390 can be raised or lowered (vertically indexed) within chamber walls 388 that contain material 344 dispensed by material dispenser 342. In certain embodiments, the bed 390 can remain fixed, and optics of the final imaging assembly 320 can be vertically raised or lowered. Material distribution is provided by a sweeper mechanism 392 that can evenly spread powder held in hopper 394, being able to provide new layers of material as needed. An image 6 mm wide by 6 mm tall can be sequentially directed by the movable mirror 386 at different positions of the bed.
When using a powdered ceramic or metal material in this additive manufacturing system 300, the powder can be spread in a thin layer, approximately 1-3 particles thick, on top of a base substrate (and subsequent layers) as the part is built. When the powder is melted, sintered, or fused by a patterned beam 319, it bonds to the underlying layer, creating a solid structure. The patterned beam 319 can be operated in a pulsed fashion at 40 Hz, moving to the subsequent 6 mm×6 mm image locations at intervals of 10 ms to 0.5 ms (with 3 to 0.1 ms being desirable) until the selected patterned areas of powder have been melted. The bed 390 then lowers itself by a thickness corresponding to one layer, and the sweeper mechanism 392 spreads a new layer of powdered material. This process is repeated until the 2D layers have built up the desired 3D structure. In certain embodiments, the article processing unit 340 can have a controlled atmosphere. This allows reactive materials to be manufactured in an inert gas, or vacuum environment without the risk of oxidation or chemical reaction, or fire or explosion (if reactive metals are used).
Other types of light valves can be substituted or used in combination with the described light valve. Reflective light valves, or light valves base on selective diffraction or refraction can also be used. In certain embodiments, non-optically addressed light valves can be used. These can include but are not limited to electrically addressable pixel elements, movable mirror or micro-mirror systems, piezo or micro-actuated optical systems, fixed or movable masks, or shields, or any other conventional system able to provide high intensity light patterning. For electron beam patterning, these valves may selectively emit electrons based on an address location, thus imbuing a pattern on the beam of electrons leaving the valve.
In this embodiment, the rejected energy handling unit has multiple components to permit reuse of rejected patterned energy. Relays 228A, 228B, and 22C can respectively transfer energy to an electricity generator 224, a heat/cool thermal management system 225, or an energy dump 226. Optionally, relay 228C can direct patterned energy into the image relay 232 for further processing. In other embodiments, patterned energy can be directed by relay 228C, to relay 228B and 228A for insertion into the energy beam(s) provided by energy source 112. Reuse of patterned images is also possible using image relay 232. Images can be redirected, inverted, mirrored, sub-patterned, or otherwise transformed for distribution to one or more article processing units. 234A-D. Advantageously, reuse of the patterned light can improve energy efficiency of the additive manufacturing process, and in some cases improve energy intensity directed at a bed, or reduce manufacture time.
Other manufacturing embodiments involve collecting powder samples in real-time in a powder bed fusion additive manufacturing system. An ingester system is used for in-process collection and characterizations of powder samples. The collection may be performed periodically and the results of characterizations result in adjustments to the powder bed fusion process. The ingester system can optionally be used for one or more of audit, process adjustments or actions such as modifying printer parameters or verifying proper use of licensed powder materials.
In certain embodiments, an additive manufacturing machine such as disclosed herein can be programmed to adjust laser power flux and dwell time, print order among other machine parameters during the manufacturing process, as well as support structure, orientation, and overall part topology among other geometrical parameters during the part pre-processing. These adjustments can be guided with reference to an physics model optimized for additive manufacturing processes, including but not limited to powder bed fusion.
For example, in one embodiment the additive manufacturing process can be simulated using data related to the Computer Aided Design (CAD) geometry for the powder bed, material type, printer model (or printer capabilities), and desired resultant material properties such as stress distribution, thermal warpage, or crystal structure. Simulation results can be compared to a part material specification, and power flux, dwell time, and print order along with other geometrical parameters such as part orientation, support structure, and part topology can be adjusted in the simulated machine and the simulation repeated. Machine learning algorithms can allow for previous simulations (and the results of previous experiments stored in databases) to be accessed by these algorithms to minimize the number of cycles required, and allow for faster convergence on the optimum manufacturing parameters to create the desired parts with the desired properties.
If part functionality requirements are also known, another level of simulation can be carried out to optimize the design of the part for both the end use case, and for the additive manufacturing process. As an example, a part could benefit from various levels of internal pre-stressing developed/imbedded during the additive manufacturing process, the use of which would be realized in the end-use application. A stress state throughout the part can be specified to be within a specific tolerance, and in certain embodiments a part can be manufactured to have a two or three-dimensional stress map within a given tolerance or spatially defined set of tolerances.
Once a simulation is performed with sufficient accuracy, the results will typically hold for repeated machine runs. This is particularly useful for high throughput techniques of additive manufacturing such as disclosed herein, since results of one simulation can be re-used multiple times.
Furthermore, a single print bed simulation might contain multiple parts of different types. If a part of a given type has been simulated or printed before, initial conditions can be set for that part using the pre-solved conditions from past parts. Alternative solutions are to design all parts to interconnect in the same way such that they are separable enabling the division and superposition of simulation results. Once a solution for a given part is obtained, if packed in an appropriate configuration in the bed, it is possible to apply the theory of superposition to packing and applicable analyses. Additive manufacturing process for a part in one bed can be applied to that in a completely different bed, surrounded by completely different parts. Modeling and manufacture efficiency can be improved in some embodiments by ensuring that appropriate boundary conditions are applied for the part such that its connection to surrounding parts is always the same.
As an example of pre-processing and design optimization, producing a part with minimal residual stresses using simulation, topology changes, and machine parameter modification can be accomplished using one or more steps of the following described method. First, a Computer Aided Design (CAD) model, material type, Additive Manufacturing (AM) process type, and desired part design and residual stress expectation are input into an optimization algorithm of a simulation. This optimization algorithm can utilize background data from previous simulation runs to inform the results of the current simulation. Knowing the type of additive manufacturing process and part material/physical properties, processing parameters can be derived from previous results. The AM process is then simulated and resultant stress distribution, crystal structure, and other relevant properties are evaluated. Based on the design requirements, the AM process parameters such as laser power, dwell time, or event timing can be modified, and the process re-simulated. Alternatively, the part (including support structure) can be topologically optimized, with or without modification to the processing parameters, and the AM process re-simulated. If simulated parts meet the desired specification, the process is complete and the resulting process parameters can be passed to the AM machine to carry out the manufacturing process. If simulated parts are not acceptable, then the data can be fed back and the process repeated.
As will be understood, while the foregoing described modeling techniques can be done on on-board computers in the additive manufacturing machine, it could also be offered as a remote service. Due to the computationally intensive nature of these calculations, the process would greatly benefit from the ability for users to submit “jobs” to an available super computer that could process the data and return the result after the iterations were completed and a solution converged upon.
In another embodiment, additive manufacturing can include in-process monitoring with correctional strategies. Post-process data collection and comparison to experiments can also supported.
In-process monitoring relies on the use of a suite of sensors such as, but not limited to, vision, IR, thermocouples, and pressure sensors to evaluate melt pool characteristics, powder bed/base plate/build chamber temperatures, thermal radiation profiles, and system gas pressure among many other parameters. Evaluating and determining if the build process is proceeding correctly and taking the appropriate corrective actions helps ensure that parts created are true to form, and maintain the desired shape.
Sensor monitoring for in-process control can monitor both an area currently being printed (i.e. illuminated with an energy beam such as laser light or e-beam), and the next area to be printed. Careful measurement of the next location to be printed, correlated with simulation and table value lookup can aid in adjusting the print schedule on the fly, adjusting the delivered power flux to match the temperature in upcoming zones to alleviate over/under melting scenarios which lead to degradation in part resolution and increase of thermally induced stresses. Other examples of potential actions include selective changes to the heating/cooling of zones in the chamber walls, substrate, or ceiling to spatially manage heat loss.
In those embodiments where a vision system and in-process monitoring are able to visually evaluate how effectively the pixels of a given layer were printed, actions can be triggered such as reprinting of a pixel prior to new layer going down, or extra attention/action in following layer to ensure that that pixel printed as desired. In this method, random and unforeseen errors introduced during the printing process are mitigated.
Post processing analysis allows for the correction of computational models and corrective action algorithms to better reflect the real-life end result. Destructive stress analyses (e.g. tensile, compressive, torsion tests) can be used, as well and non-destructive stress analysis techniques that allow evaluation of parts. This can include calipers for key dimensions, coordinate measuring machines (CMM), vision systems for metrology, and some methods that allow for deep penetration into materials such as Neutron Diffraction (ND). Neutron diffraction in particular is a method that can be used to determine internal stress distributions in crystalline structures by evaluating how neutrons penetrate and scatter off of atoms in the crystalline lattice. It can penetrate relatively deep (60 to 100 mm), and can be used to measure stress distributions in complex shapes made from steel, aluminum, and titanium. Feedback from ND is useful for the evaluation of additively manufactured parts since it can be used to calculate the internal stress fields of a part if the dimensions for penetration depth are correctly observed.
Many modifications and other embodiments of the invention will come to the mind of one skilled in the art having the benefit of the teachings presented in the foregoing descriptions and the associated drawings. Therefore, it is understood that the invention is not to be limited to the specific embodiments disclosed, and that modifications and embodiments are intended to be included within the scope of the appended claims. It is also understood that other embodiments of this invention may be practiced in the absence of an element/step not specifically disclosed herein.
The present disclosure is part of a non-provisional patent application claiming the priority benefit of U.S. Patent Application No. 62/289,824 filed on Feb. 1, 2016, which is incorporated by reference in its entirety.
Number | Date | Country | |
---|---|---|---|
62289824 | Feb 2016 | US |