Patent Application
20230386017
The present description relates to techniques of analysing an industrial processing process, in particular industrial processing processes carried out by processing machines, in particular laser processing machines, namely industrial processes carried out using laser, such as laser cutting.
One or more embodiments may be applied, for example, in contexts of quality control of laser processing processes.
Processes of processing objects that use laser beams comprise guiding and focusing on the object being machined a laser beam emitted by a respective laser source. Processing may include, for example, laser cutting or laser welding. Laser processing machines provided for carrying out the aforesaid machining may comprise, for example, machine-tool devices, such as laser processing heads.
It is deemed important to monitor the welding process continuously during the entire laser processing process so as to guarantee the processing quality, for example the welding quality.
At the same time, laser processing processes (e.g., laser cutting) are complex processes, of which it is difficult to provide an adequate description in closed form that will enable analytical description of the evolution and quality thereof starting from the information available on the machine.
Among the conventional solutions the following documents may, for example, be mentioned:
EP 1464435 A1 that discusses a method for controlling the quality of an industrial laser process, in which quality is evaluated based on signals emitted by means for detecting the radiation emitted by the treatment area, without the need for a comparison with predefined reference signals indicating a good quality process;
WO 2020/104103 A1 that discusses a system for monitoring a laser machining process for machining a workpiece, comprising: a computing unit which is designed to determine an input tensor based on current data of the laser machining process and to determine an output tensor based on the input tensor using a transmission function, said output tensor containing information on a current machining result, wherein the transmission function between the input tensor and the output tensor is formed by a trained neural network;
WO 2020/104102 A1 that discusses a system for detecting machining errors for a laser machining system for machining a workpiece, the system comprising: a detection unit for detecting image data and height data of a machined workpiece surface; and a computing unit, wherein the computing unit is designed to generate an input tensor based on the detected image data and height data and to determine an output tensor based on the input tensor using a transfer function, said output tensor containing information on a machining error.
Notwithstanding the vast activity in the above area, as witnessed, for example, by the various documents listed previously, further improved solutions are desirable.
An object of one or more embodiments is to contribute to providing such an improved solution.
According to one or more embodiments, the above object may be achieved by means of a method of analysis having the characteristics set forth in the ensuing claims.
One or more embodiments may regard a corresponding apparatus for industrial processes. A processing machine for laser cutting may be an example of such an apparatus.
One or more embodiments may relate to a corresponding computer program product loadable into the memory of at least one processing circuit (e.g., a computer) and including software code portions for executing the phases of the method when the product is run on at least one processing circuit. As used herein, reference to such a computer program product is understood as being equivalent to reference to a computer-readable medium containing instructions for controlling the processing system in order to co-ordinate implementation of the method according to one or more embodiments. Reference to “at least one computer” is intended to highlight the possibility of one or more embodiments being implemented in modular and/or distributed form.
The claims form an integral part of the technical teaching provided herein with reference to the embodiments.
Various embodiments present the advantage of exploiting a data-driven empirical approach based upon a parametric model, where the structure of the functional links between input and output can be uncoupled from the structure that the constitutive equations of the system or process would have.
One or more embodiments may use an artificial-neural-network processing in which the parameters are determined via a training procedure on a database at input and accordingly at output so as to pursue minimization of a loss function.
One or more embodiments may use techniques of deep learning (DL) that, by making significant transformations on the data through filtering of a series of layers, are able to “learn” useful representations of the available data themselves.
One or more embodiments may use at least one deep neural network (DNN) as a sort of multi-stage operation of “distillation” of the information, in which the latter proceeds through a series of successive filters, coming out thereof increasingly “purified”, namely targeted for a certain application.
One or more embodiments may use procedures of image recognition through convolutional neural networks (CNNs).
In one or more embodiments, the raw data of the machine may be used to produce and analyze images to be supplied to subsequent user stages.
In one or more embodiments, the method makes it possible to intervene in a short time to identify, from the data, any possible malfunctioning of the machine.
One or more embodiments will now be described purely by way of example, with reference to the annexed drawings, wherein:
In the ensuing description, one or more specific details are illustrated, in order to enable an in-depth understanding of examples of embodiments of the present description. The embodiments may be obtained without one or more of the specific details or with other methods, components, materials, etc. In other cases, known operations, materials, or structures are not illustrated or described in detail so that certain aspects of the embodiments will not be obscured.
Reference to “an embodiment” or “one embodiment” in the framework of the present description is intended to indicate that a particular configuration, structure, or characteristic described with reference to the embodiment is comprised in at least one embodiment. Hence, phrases such as “in an embodiment” or “in one embodiment” that may be present in one or more points of the present description do not necessarily refer precisely to one and the same embodiment.
Moreover, particular conformations, structures, or characteristics may be combined in any adequate way in one or more embodiments.
The references used herein are provided simply for convenience and hence do not define the sphere of protection or the scope of the embodiments.
As used herein, the term “image” refers to a digital image, namely to the numeric representation of a two-dimensional image, in particular a bitmap representation whereby the image comprises a matrix of dots, referred to as pixels, the colour of which (in scales of grey or other colours) is encoded via one or more numeric values, denoted as bits.
For instance, the above apparatus 10 may comprise:
As illustrated in
In what follows, reference will be made, for the sake of simplicity, to an apparatus 100 comprising a processing machine 10 with mobile structure 12 of a cantilever type with three cartesian axes (denoted by the letters X, Y, Z), also referred to as cartesian machine. It is noted that the type of structure discussed is in no way binding or limiting; in fact, the solution discussed can be adapted to structures of some other type, e.g., ones with six degrees of freedom (with redundant axes), or ones that follow only two-dimensional trajectories.
Once again for the sake of simplicity, in what follows, reference is mainly made to a laser end effector 14 configured to carry out cutting operations W, being otherwise understood that such a type of laser processing is provided purely by way of non-limiting example. In various embodiments and variants, the apparatus 10 can use one or more end effectors configured to carry out (virtually) any type of laser processing or in general also other types of processing of industrial processing processes.
As mentioned, the sensors of the set of sensors 30a, 30b, 30c are configured to sense values of process parameters, namely quantities and other parameters of the processing process that is carried out by the laser processing apparatus 10, namely to sense measurements of quantities indicative of operation of one or more parts of the apparatus 10 itself. The sensors of the set of sensors 30a, 30b, 30c are likewise configured to generate respective measurement signals or data R that indicate the values of the quantities measured and to transmit these signals R to the processing module 20.
As exemplified in
It is noted that such a composition of sensors of the set of sensors 30a, 30b, 30c is provided purely by way of non-limiting example. In one or more embodiments, the set of sensors 30a, 30b, 30c may comprise, in addition or as an alternative, at least one sensor of a type of sensor taken among known types of sensors, such as those of an inertial type (e.g., a triaxial accelerometer, a gyroscope, etc.), a temperature sensor coupled to the tip of the end effector 14, and a laser power sensor.
The sensors belonging to the set of sensors 30 preferably provide at output one-dimensional signals, in particular ones representing “raw” data, namely sensed but not processed. As discussed in what follows, said one-dimensional signals are preferably then transformed into two-dimensional signals in two-dimensional maps in which one dimension is time, for example a time-frequency map or a time-scale map.
However, it is also possible to make use of sensors that directly provide at output two-dimensional signals. For instance, the sensor (photodiode) 30a could be replaced or accompanied by a spectrometer, or mini-spectrometer, which senses the radiation emitted by the process and directly supplies at output a two-dimensional time-frequency signal. In this case, no processing is necessary for the two-dimensional representation.
The above signals, whether one-dimensional or two-dimensional, supplied by the sensors 30 comprise signals that vary in time.
For the sake of simplicity, in what follows the term “sensor 30” is used in the singular, being otherwise understood that what is described for the sensor 30 may be extended, for example, to any type of sensor, to more than one sensor, and to each sensor in the set of sensors.
The sensor 30 may likewise comprise those of a “soft” or virtual type, comprising sensors or sensor sets that measure other quantities and that, via processing, obtain the estimate of a quantity representative of the process. For instance, the proximity sensor 30b, where in particular “proximity” is referred with respect to the metal sheet, for example a capacitive sensor, also referred to as “gap sensor”, can be used to measure a count frequency electronically, while the software processing that is carried out on board a CNC control unit 22, described more fully hereinafter, makes it possible to derive from the above frequency the distance between the tip of the laser head 14 and the surface of the metal sheet in the work region 40.
The operation of sensing also comprises acquisition of process parameters, for example parameters linked to the state of machine configuration 10 to carry out a certain processing, such as the type of material and thickness of the material being machined, the type of process gas, the type of end effector and lens, the type of laser, the type of machine, etc. These parameters can be “sensed”, for example, by accessing locations of a memory of the processing module 20 in which the respective values are stored.
The signals and parameters acquired may be temporally divided according to time intervals referred to as “zones of interest”. For instance, the zone of interest may correspond to a specific interval during processing; for example, from when the machine 10 starts to cut, signals are acquired for a finite time interval (e.g., 1 second). The zone of interest may also correspond to keeping sensing active throughout the duration of cutting, in real time. Segmentation of the data regarding the signals and/or parameters acquired enables intervention in the shortest time possible by identifying from the data any possible malfunctioning of the machine. Furthermore, for example it is possible to sense simultaneously different values via one or more different types of sensors.
The processing module 20, as mentioned, is hence configured to be coupled to the apparatus 10, in particular to the sensor 30 and to the motors of the mobile structure 12 in order to drive movement of the end effector 14 with respect to the work region 40 so as to carry out a movement according to the axes X, Y, Z.
It is noted that for the sake of simplicity in what follows the expression “movement of axes X, Y, Z” will be understood as referring to the operation of driving the motors and/or actuators coupled to the mobile structure 12 so as to move the end effector according to the aforesaid one or more axes X, Y, Z.
As has been mentioned, once the sensor 30 transmits the measurement data R to the processing module 20, the module 20 can be configured to process the measurement data R, for example so as to:
As exemplified in
In the present description, “pattern recognition” is meant as automatic recognition of patterns and regularities in data, in particular the data supplied at input to the stage 26. Even though in the present description reference is made to pattern recognition obtained via neural networks, application of the solution described may also include pattern recognition via rule-based systems, classic fuzzy systems, Bayesian systems, fuzzy neural-network systems. Reference is moreover made herein to a pattern recognition that envisages a training phase in which a training dataset is used to train the recognition model, prior to a recognition phase via the trained model, implemented during processing, also referred to in what follows as “inference phase”, in particular with reference to the use of pattern recognition via neural networks.
For instance, the CNC unit 22 in the processing module 20, comprises the following (represented as dashed boxes within the stage 22): a first processor 222; a second processor 224; and a servo-drive module or card 226, namely a card comprising one or more servo-drives, servo-amplifiers, or servo-control modules for the motors/actuators of the machine 10.
As exemplified in
As mentioned, the CNC unit 22 in the processing module 20 controls operation of motors and actuators for moving the axes X, Y, Z of the mobile structure 12, according to programs, or sequences of programming instructions P, pre-set as a function of the requirements of processing of the piece, and in a co-ordinated way. Such programs P are prearranged for moving the mobile structure 12 so as to displace the end effector 14 with respect to the envelope 40 illustrated in
As represented in
As exemplified in
The pattern-recognition stage 26 in the processing module 20 may comprise a set of layers of artificial-neural-network processing 260, 270.
The pattern-recognition stage 26 may be configured to provide one or more recognition signals Q as classification signals of the processing in progress or carried out according to the categories regarding the “state” or “quality” of the aforesaid processing. To such classification signals there may be associated a set of information (e.g., in the form of a text report or a text string), which may regard an evaluation of a level of processing quality or an operation to be carried out based on such an evaluation (such as rejecting the processing if it is deemed of poor quality). Once obtained, the information associated to the classification signal Q may then be supplied to user devices for different applications, which can carry out pure signalling of the information, locally and possibly remotely, or also carry out automatic checks or activate processing feedback based on this information and possibly in order to correct the process.
The set of information in the recognition signal Q, processed by the pattern-recognition stage 26, may hence comprise a classification of the signal (e.g., an ok/not-ok binary classification) in a series of categories comprising, for example: cutting quality, report on percentage cutting with localization, cutting profile. In another example, the information may signal a possible wrong loading of the metal sheet to be machined into the apparatus 10.
The above set of information, specifically the aforesaid processing classification, in the recognition signal Q supplied by the pattern-recognition stage 26, may then be supplied, for example, to one or more of the following:
The server SV can communicate with all the stages in the processing module 20 to facilitate downloading of updates of software implementation of operations of the method, such as new versions of the software of the neural-network processing stage 26. Likewise, the neural-network processing stage 26 can send, for example via the representation stage 24 or the interface unit 21 (or directly), data gathered in field to be added to a remote database on the server SV containing data to be used to train the networks themselves, in order to render subsequent data-processing operations more robust or to facilitate analysis of new quality-control profiles.
The processing module 20 can thus be configured to exchange instructions and data P, R, Rf, W, at input and output, with users, for example, Internet networks, with communication modalities per se known, as discussed in greater detail in what follows.
For the sake of simplicity, principles underlying one or more embodiments are discussed in what follows with reference mainly to the case of example in which the processing is applied to a set of measurement signals R comprising:
It is otherwise understood that the foregoing discussion is provided purely by way of non-limiting example insofar as the aforesaid representation of data 24 may extend to any number and type of measurement signals coming from other types of sensor (position error from the encoder, distance of metal sheet from the proximity sensor, etc.).
Represented via a flowchart in
It is clear that in a simple embodiment it is possible to start from a first signal R1 that is deemed to represent the state of the process and that has a given rate of variation in time, assign it to the representation 242, and then assign the other signals of the other sensors available to the representation 244, if they present a slower variation or are rendered slower, for example via filtering, and to the representation 246, if they are constant in the observation window. Of course, the operation 240 may also be implemented via a module in the stage 24 that evaluates the distinction criteria during operation. In general, the data-sorting operation 240 represents the above assignment to subsets, or in any case routing of the signals of the sensors towards the corresponding representation operations according to a criterion of distinction (or classification) and may be considered optional or implicit if the signals of the sensors are sent on to the respective representations in a predefined way. This distinction preferably takes into account the rapidity of variation of the signal, but in general envisages selection of a first signal, R1, for the operation 242 that is considered apt to provide a signal indicative of the state of said industrial process as a result of said pattern-recognition operation 26, in particular with neural-network processing, in the example a classification operation via neural network. The first signal R1 will in itself have a rapidity of variation of its own, the signals sorted into the operations 244 and 246 have a rapidity of variation slower than the signal R1 (or rendered slower, for example via filtering) or are even constant in the observation time window (e.g., state parameters);
a second operation of representation 244 of low-dynamic signals, the second representation operation 244 comprising receiving from the sorting stage 240 a set of signals labelled as low-dynamic signals Rl, namely signals whose variations within the observation window are slower than those of the signals labelled as high-dynamic signals Rh, and representing them graphically as an image by extracting a representative value over a time interval coinciding with the time interval of representation of the first signal R1 or comprised therein, to obtain as a result a second digital image Rlf. It is pointed out how a signal labelled as low-dynamic signal may be obtained, from a sensor that in itself supplies at output a signal with higher dynamic, via filtering, for example low-pass filtering, or sub-sampling, or via other operations that render it a signal with lower dynamic;
a third operation of representation 246 of constant-dynamic signals, the third representation operation 246 comprising receiving from the sorting stage 240 a set of signals labelled as constant-dynamic signals Rc, namely signals the value or indication of which is constant or does not present appreciable variations within the observation window, for example parameters indicative of the state of the machine, and representing them graphically as image by extracting a representative value over a time interval equal to or shorter than the time interval of representation of the first signal R1, which usually corresponds to the above constant value or indication, obtaining as a result a third digital image Rcf; and
For instance, in the simplified case under examination, the sorting operation 240 applied to the signals R1, R2, R3 may label:
As has been said, “high-dynamic signals” and “low-dynamic signals” are meant as signals that vary more or less rapidly, in particular with respect to one another, in the observation window. As has been said, high-dynamic signals are in general the process signals useful to be employed in the pattern-recognition stage 26. Hence, low-dynamic signals are signals that have a prevalent frequency content with lower frequency and possibly a limited bandwidth in the observation window.
By way of example, it may be known that the signal of the photosensor 30a is suited to providing information on the laser welding process, and so this signal is labelled so as to be routed to the operation 242. The signals to be labelled as R2 are selected provided that they present a slower variation in the interval, such that, for example, selection of the average value will represent the quantity measured in the observation window.
As discussed herein, the term “dynamic”, whether low or high, refers to the signal in relation to the state equations that describe the dynamic system representing the sensor or measuring instrument that supplies the measurement signal. For instance, the dynamic response of such a dynamic system is a function of the eigenvalues of the respective state matrix, where the relative position of such eigenvalues in the complex plane determines the bandwidth and spectral content of the measurement signal (within the cut-off frequency).
The Inventors have noted that signals with reduced bandwidth and spectral content distributed in a restricted neighbourhood around the zero-frequency DC component are tendentially not apt for a representation of a time-frequency (or time-scale) map type, given that this representation would lead to a substantially uniform image and hence an image with low information content associated thereto, with consequent potential complexity of processing by the neural network.
Hence, given a signal R1 selected for a map representation where at least one dimension is time, for example a time-frequency map or a time-scale map, once the high-dynamic signal is defined, a low-dynamic signal, to be labelled as R2, has a spectral content prevalently at lower frequencies than said signal R1 and possibly a narrower bandwidth. In some embodiments, the frequency values associated to the prevalent spectral content, e.g., the band-centre frequency, of a high-dynamic signal differ by at least one order of magnitude from the frequency values associated to the spectral content of a low-dynamic signal. In variant embodiments, this difference is of a number of orders of magnitude. In general, the aforesaid energy content to define high and low dynamics is evaluated but for the DC component. For instance, a sensor 30, such as a photodiode that receives the radiation of a laser process, may have frequencies that range from the DC frequency to tens of kilohertz, for example 0-12.5 kHz, based on the sampling frequency applied, in the example 25 kHz, whereas a temperature-measuring signal may have a frequency of 0.1-1 Hz. The error on the pressure of the gas and the error on the stand-off, as likewise the trajectory acceleration itself or the errors of tracking of the trajectory axes, present an energy content that is, for example, prevalently concentrated in the range 0-30 Hz. In this case, the sampling frequencies are around 500 Hz.
The representation processing 24 may be divided into a number of stages, namely logic or hardware modules corresponding to the operations 240, 242, 244, 246, 248, being otherwise understood that such a representation is provided purely by way of non-limiting example. In some variant embodiments, moreover, operations discussed in relation to a certain stage could be carried out in another and/or the data of the sensors could be processed in a single stage 24 in the processing circuitry of the control unit 20.
As exemplified in
Transform operations per se known suited to being applied in block 242 comprise, for example in block 2422, at least one of the following:
It may be noted in this regard that it can be said that the CWT gives rise to a so-called scalogram, which gives the amplitudes as a function of time and scale; however, the scale can be brought back to a frequency via a simple further transformation. It may hence be said that more in general the solution described herein applies to representations in maps, the axes of which give the time and a quantity representative of a frequency: in the case of STFT, the frequency; in the case of CWT, the scale or a frequency value calculated based on the scale.
As mentioned, the above transform operations, in block 2422, do not become necessary in the case of a high-dynamic signal Rh supplied at output from the sensor directly as two-dimensional signal, for example using a mini-spectrometer that intrinsically supplies at output a time-frequency map of the signal and hence an image Rhf, in which case the representation made in block 242 corresponds only to the graphic representation of the two-dimensional signal in the format of the digital image Rhf.
Application, instead, of the aforesaid representation operations, in particular transformation operations, in the first processing stage 242 may comprise, in block 2420, segmenting the signal on which these operations are applied into segments corresponding to observation windows, which may also coincide with zones of interest. These segments may be partially overlapping, for example segmented, via the use of a moving (observation) window K of width H.
Indicated in both of the images are the time axis t and the frequency axis f, whereas the colour intensity indicates the value or amplitude of the transform, STFT or CWT, respectively.
It may be noted that the resolution of the STFT varies as a function of the predefined size, namely width H (see
In order to overcome this trade-off, the CWT (Continuous Wavelet Transform) can provide both good time resolutions for high-frequency events and good frequency resolutions for low-frequency events.
In particular, a CWT that uses a complex Morlet mother wavelet can favour attainment of satisfactory values in a joint way for both types of resolution.
In variant embodiments, the two transforms, STFT and CWT, can be used in a complementary way, for example applying the STFT in the cases where the computational complexity of the CWT risks being excessively burdensome for the control unit 20.
It is noted that in one or more embodiments both types, SFTF and CWT, of data transformation processing can be used alternately, on account of the fact that some signals could have a high sampling frequency and others a lower sampling frequency.
The size of the signal-observation time windows K may range from a few milliseconds for the CWT to some tens of milliseconds or a hundred milliseconds for the STFT.
For instance, in the case of the CWT, considering a time window of 128 samples and 128 scales, the width of the window is approximately 5 ms (128/25000 of a second), which is the time necessary to produce the 128×128 image of a single frame.
In the case of the STFT, the transform in itself is simpler, but the composition of the image is more complex. Aiming to generate once again a 128×128 image for a single frame, each line or row represents an FFT. Along the time axis 128 FFTs are computed, each of which undergoes a shift with respect to the previous one by a certain number of samples, for example eight. Each FFT should be computed on a number of points that is twice the number of points on the frequency axis (once again 128), hence: 128×2=256. For reasons of resolution, it is preferred to compute the FFT on a vector with a number of points that is a multiple of 256, for example according to a factor equal to 4 (hence on a 1024 vector) and then bring the dimension back to the original value via re-sizing of the transform. The resolution thus obtained is better than the one that would be obtained by applying the FFT on the 256-point vector. In the example considered, to generate, using the STFT, the 128×128 image of a frame, it is necessary for a number of samples to have elapsed equal to: (128−1)×8+4×(128×2)=2040 samples. This results in a length of time 2040/25000 equal to approximately 80 ms. If a laser cutting operation is carried out at a rate for example of 30000 mm/min, in the case of the STFT, a distance of 147 mm would be covered before a frame is generated, whereas a distance of only 9 mm would be covered in the case of CWT.
The result of the calculation of time, performed for the signals sampled at 25 kHz, changes slightly in the case of signals sampled at 1 kHz, having considered performing a micro-interpolation of the quantities acquired at 1 kHz with a micro-interpolation factor of 25 so as to extract in any case samples every 1/25000 s.
Linear interpolation has been discarded because it gives rise to spurious disturbance lines on the map of the image.
The interpolation adopted is the cubic one, which requires introduction of a delay of 4 samples, to evaluate the 4 coefficients of the interpolation curve.
The total time required for generation of a frame becomes:
The processing block 2420, in the case provided by way of example of the two-dimensional signal produced by a mini-spectrometer, may comprise, instead of the segmentation and windowing operations, buffering and segmentation operations in order to render the width of the time dimension of the time-frequency map homogeneous with the width of the time dimension of the time-frequency/time-scale map constructed by the processing block 2422 starting from one-dimensional signals Rh.
As exemplified in
Since the segmentation window to obtained segments Rli has a predefined size and is, for example, the same window as that used in the segmentation operation of block 2420 or an observation time window comprised therein, the representation as second image Rlf refers to a time interval equal to or shorter than the time interval of representation of the first signal R1.
The second digital image Rlf may be represented as an image comprising a sequence, corresponding to the sequence of the segments Rli, and hence to the corresponding different observation windows, of markers that “picture” a sequence of positions of the aforesaid knobs of the virtual counter, as shown, for example, in
The Inventors have noted that such a differentiated processing enables transformation into multidimensional data of the time data to which it would otherwise be difficult to apply the transforms used for the high-dynamic signals with adequate resolution or dynamic range, given that these low-dynamic signals might carry a negligible frequency content.
Once again, the use of virtual indicator knobs, e.g., pointers, which indicate a value of measurement on a graduated scale, instead of simple strings to encode the information of the average value of the signal, means that numbers that are close to one another, for example, 19.9 and 20.1, maintain a certain degree of closeness if displayed as positions of a knob counter, namely one with a circular graduated scale with respect to which the indicator knob indicates values by turning around an axis of its own, whereas, if they were displayed as numeric values, a similar behaviour at the processing level would be associated to completely different image details so that conversely, based on the images, erroneous analyses of the processing quality would be obtained.
It is noted that use of the same observation window K both for the high-dynamic signals and for the low-dynamic signals facilitates a relation of association to be maintained between the respective segments Rhi, Rli, so that the “slow” signals for the segments Rli will be associated to the “fast” signals for the segments Rhi of each acquisition (or acquisition window). In the example considered, the first fast signal R1 of a photodiode is associated to the second signal R2 indicating the gas temperature/pressure, whereas in another example a fast proximity-sensor signal (e.g., the stand-off of the capacitive sensor) is associated with the speed of the axes X, Y, and Z and the rate on the cutting profile, for example as average values, in the same observation window K.
In variant embodiments, the block 2444 may further comprise generating the digital image Rlf by associating a frame of asymmetrical or irregular shape to each marker that indicates a position of the knobs on the virtual counter, as exemplified in
The purpose of such a frame is, for example, to facilitate subsequent pattern-recognition stages 26, in particular classification stages, in recognizing properly the position of the markers themselves.
As is known to persons skilled in the branch, neural networks, in particular those of a convolutional (CNN) type, may be configured to learn patterns that are invariant to displacement.
The Inventors have observed that framing of the image of the markers Rlf with a respective frame of an asymmetrical shape, in particular different from the other frames of the other markers Rlf, in particular marker pairs in the example, regarding other quantities measured in the same observation window, can exploit the capability of the neural network to learn spatial pattern hierarchies.
In particular, the use of frames of an asymmetrical shape is aimed at facilitating unique identification of a given quantity to be processed, so that, in one and the same composite image, each frame-marker set for a given quantity will differ from the others; otherwise, the neural network could interpret multiple different positions as having the same meaning.
For instance, the second matrix of transformed data Rlf may comprise a sequence of markers (framed by respective frames, which differ from one another) that virtually “picture” the positions of the setting knobs of the virtual counter regarding different quantities represented by low-dynamic signals Rl, for example temperature, speed of axes of the conveying system, etc. In the figure, this sequence is arranged, for example, along the frequency axis, each marker associated to a given quantity representing its average value in a time interval that corresponds to or is shorter than the observation window of the first signal.
In variant embodiments, the sorting stage 240 may be configured to select the second processing block to process both low-dynamic signals RI and high-dynamic signals Rf.
As exemplified in
As a result of such a processing operation 246, it is hence possible to produce the third image Rcf for the constant-dynamic signal Rc, which comprises the sequence of icons selected to encode the data Rcf sensed in each observation window K.
In the present description, “digital icon” is meant as a numeric representation of the two-dimensional shape associated to a somewhat stylized pictogram of the element represented.
For instance, the third image may comprise a sequence of multiform icons arranged linearly.
As exemplified in
For instance, production of a composite image Rf may comprise superimposing 248 on the first image Rhf at least one other image from between said second image Rlf and said third image Rcf.
In superimposing the images Rhf, Rlf, Rcf to produce the composite image Rf, the block 248 can use a position grid G, in which to arrange the at least one other image Rcf, Rlf in a way aligned with the “background” image of the high-dynamic signals Rhf, for example according to a position grid G having three rows or bands.
For instance, as exemplified in
It is noted that the above arrangement of the images in the composite image Rf is provided purely by way of non-limiting example. In variant embodiments, the way in which the images are arranged in the grid G may be different; for example, the positions of the second and third images could be switched.
Using time windows having one and the same width H (which is the same or smaller for the second and third images Rlf, Rcf), in superimposing at least one between the second and third images Rlf, Rcf on the first image Rhf to produce the composite image Rf, the result is that, in the composite image, the first digital image, the second digital image, and/or the third digital image refer to one and the same window H.
For instance, as exemplified in
The icons of the sequence of icons of the third image Rcf in the row G1 may vary according to the window H, in the composite image Rf, even though in general, given that the icons of the row G1 encode constant state information, linked to the machine configuration or to the processing-program settings, more often than not the icons change from program to program but not during runtime of the individual processing program.
It is moreover noted that the composite image Rf may comprise a number of composite images arranged adjacent to one another in a grid, or matrix, for example a quadrangular one, namely by forming rows and columns of adjacent composite images, where a number of composite images can be obtained by processing in parallel data received from the sensor 30 in successive processing phases (zones of interest) or different groupings of data R1, R2, R3 gathered by different types of sensors 30a, 30b, 30c, 30d of the set of sensors 30.
In this regard,
In particular, as exemplified in
On each of the digital images Rf1, . . . , Rf9 there can then be overlaid further digital images obtained by applying:
In this way, the digital images Rf1, . . . , Rf9 become composite images Rf1, . . . , Rf9 ready for being processed by the neural network 26.
In variant embodiments, processing by the processing stage 24 is carried out on data received from the sensor 30 in successive processing phases, as discussed previously.
As exemplified in
For instance, the composite image Rf of
As it may be noted, the first row G1 remains identical in so far as the plurality of composite images refers to one and the same processing program in which the configuration of the machine carrying out processing does not change.
As exemplified in
In variant embodiments, the set of transformed signals may also comprise a “continuous” temporal sequence of images, namely a video. For instance, the signal representations exemplified in
The method as exemplified herein may comprise training an artificial neural-network circuit on a set of composite images Rf, or training dataset, preferably associated to a class-membership index, as also specified in what follows. Since the method described herein can perform recognition, namely the inference phase, on single composite images Rf or on a plurality of composite images that refer to different first signals R1 and are arranged for example in matrix form, for instance Rf1, . . . , Rf9, the training set may accordingly comprise single composite images or a plurality of composite images that refer to different first signals R1 and are arranged, for example, in matrix form, as in
Consequently, in some embodiments, the composite image Rf represents, both in the training phase and in the inference phase, the type of input supplied to the classification processing carried out in the classification stage 26.
Hence, in general it is envisaged to apply a classification operation carried out using the pattern-recognition module 26, in particular the classification module, trained on a set of the aforesaid composite images stored in a training dataset.
Pattern-recognition processing 26, in particular artificial-convolutional-neural-network (CNN) processing, comprises computerized instruments that exploit deep-learning algorithms to carry out image-processing activities, for example recognition of objects represented within the images.
As exemplified in
In general, the processing layers of a CNN can use up to millions of parametric values, also known as weights. The weight values are “learnt”; namely, they are pre-arranged, through a training processing phase that may imply (large) training datasets. In general, the processing layers (also referred to as hidden layers) are configured to apply data processing to a tensor of images received thereby via a training phase, which can be performed in a supervised way or not, according to error-minimization targets.
As discussed herein, a CNN apt for classification processing 26 can be trained to provide a processed output signal Q using as training data one or more training datasets stored in a database, for example in a server SV.
As exemplified in
In one or more embodiments, the processing layers 260, 262, 264, 265, 267, 269, 270 may have a multilayer perceptron (MLP) architecture comprising a plurality of processing units referred to as perceptrons.
A single i-th perceptron of the plurality of perceptrons may be identified by a tuple of values comprising weight values wi, offset values bi, and an activation function P.
As exemplified in
A convolutional layer such as 262 (once again taken as a possible example) may be configured to apply an activation function on a cross correlation (sliding dot product).
Such an operation may be expressed, for example, as follows:
b=ρ(wiT·a)
As exemplified in
In other words, the features are “merged” in a synthetic way in a vector and processed to provide the pattern-recognition signal Q, in particular a classification signal.
In some embodiments, it has been found that convolutional neural networks contained in a development library known by the name of Keras, such as Inception V3 and Xception, are suited to processing/classifying the transformed data Rf, as are likewise networks such as those known by the names of ResNet50, VGG16, VGG19 and MobileNet; in other embodiments, simpler architectures yield satisfactory results.
Hence, advantageously, the method described carries out, based on sensed signals, a recognition of patterns regarding a state of the industrial process or of the product of processing by exploiting artificial neural networks that operate on images.
In particular, the method described reduces error margins of the artificial neural networks that, operating on images, can identify as being the same peak or the same features peaks or features generated by phenomena that are different but that have a similar form, this being obtained by superimposing marker values and images that represent the state on the two-dimensional maps of the signal to be processed so as to introduce differences in the images that aid artificial-neural-network processing.
In addition, the spatial arrangement of the further superimposed images provides a further reference for artificial-neural-network processing.
In a first embodiment, the CNN in the pattern-recognition stage 26 is configured to provide the recognition signal Q as signal of classification of the processing obtained from the industrial process. To this end:
In variant embodiments, the pattern-recognition stage 26 may be configured, via training with a respective training dataset, to provide one or more recognition signals Q as values of a regression, for example through an estimation of a metrological characterization of a product of processing: in particular, the signal Q can provide an estimate of a value of roughness, optionally normalized, of at least part of the product of processing.
It may be noted in this regard that in general, from the standpoint of pattern recognition, a classification operation and a regression operation are distinguished by the fact that classification operates on discrete information, providing at output, for example, classes, whereas regression operates on continuous values.
Hence, in a second embodiment, the CNN in the pattern-recognition stage 26 is configured to provide the signal Q as regression signal of parameters of the processing obtained from the industrial process. To this end:
In this second embodiment, as has been said, the signal Q supplied in the inference phase could be an estimate of roughness of processing carried out via the industrial process.
In various embodiments, the method may comprise data-collection operations for supervised training of the CNN of the pattern-recognition stage 26.
The Inventors have noted that, starting from the acquisitions of the dimensions on the axes, it is possible to draw the profiles cut and select, on each profile, one or more portions (or segments) of the end product, for example of a metal sheet that has been cut: according to the selection made, it is possible to trace back to what are the start and end of the processing time interval in which the specific portion of product has been machined. By exploiting this observation, it is possible to obtain portions of signals sensed by sensors 30 that correspond to the time interval in which the portion of product that is to be analyzed has been obtained.
Consequently, by extracting a portion of signal R1, R2, R3 in the corresponding time interval for each signal of the set of sensed signals R (in particular, given that the signals are synchronous with one another), each portion of signal R1, R2, R3 can thus be processed in the processing module 24, to generate a multiplicity of composite images Rf1, . . . , Rf9, for example as a function of the size of the portion of signal analyzed.
Each signal portion, corresponding to a respective portion on the product drawing (e.g., of the profile cut) is easy to localize and inspect on the specimens cut and can be labelled in terms, for example, of binary judgement—good/bad—of the cutting quality or else of any metrological scalar quantity, such as the roughness measured under an electron microscope.
Regarding the pattern-recognition procedure, it is emphasized how, in the training phase of the recognition model implemented by the stage 26, an operation of labelling with a label, for example of a string type and a binary type, facilitates obtaining a training set for the neural network that will provide a classification signal. The training dataset obtained using this first labelling operation may hence comprise “composite image”/“label of the membership class” pairs with which to train the neural network. The labels, for example for classifying the processing quality, comprise strings such as ‘good’, ‘bad’ or binary codes 0, 1.
As an alternative, in the case of pattern recognition that performs a regression, a second operation of labelling with a label corresponding to the class of regressions facilitates training of the CNN to infer a scalar index or value (in the example considered, a roughness) instead of the index of membership in a class.
The training dataset obtained using this second labelling operation may thus comprise “composite image”/“scalar index of quality” pairs. Hence, the method described herein also envisages that the pattern-recognition stage 26 will operate based on a classification model, in particular implemented via a CNN, trained based on a set of composite images Rf, in particular there being associated, to these composite images of said training dataset in the server SV, corresponding class membership indexes, in the specific example a quality class, which may, for example, have been entered by a technical expert. Hence, more in general, in the case of classification, the training dataset comprises composite images associated to a state or class indication corresponding to the composite image.
In variant embodiments, the method also envisages that the pattern-recognition stage will operate based on a regression model, in particular implemented via a CNN, trained based on a set of composite images Rf, in particular these composite images being associated, in said training dataset in the server SV, to corresponding scalar indexes—for example, scalar quantities measured from the process or from the product and referring to the process to which the composite image, such as the roughness of the cut, refers that corresponds to a class, in the specific example a quality class—which may, for example, have been entered by a technical expert; namely the pattern-recognition operation 26 is an operation of regression, and said property of said industrial process is a value representing said industrial process, in particular an estimate of a measurement made on the industrial process or on its product. Hence, more in general, in the case of regression, the training dataset comprises composite images associated to values of measurements made on the industrial process (or on its product) corresponding to the composite image.
It is noted that, also starting from the acquisitions themselves, it is possible to generate different composite images, and hence different training datasets, so that on the one hand there is the tendency to insert into the composite image a multiplicity of signals, leaving to deep-learning on the images (in practice to the minimization of the target function through back-propagation) the task of exploring and weighing the possible “correlations” of the various data with the target function itself, whereas on the other hand there is the attempt to reduce the size of the images at input in order to reduce the computational complexity.
In various embodiments, it is possible to adjust, also manually, a heat-map (map of activations) of the CNN, retaining the most significant values.
Hence, based on what has been described above, the method for analysing industrial processes, for example to monitor processes, described herein comprises applying to at least one sensed signal R1, R2, R3 of a set of sensed signals R that represent parameters of the industrial processing process an operation of pattern recognition 26, obtaining as a result a recognition signal Q indicative of a property of said industrial process; for example, said property may be a discrete state, such as the quality class of the industrial process, or may be a continuous value such as the estimate of a measurement of a quantity of the process or of the product, for example an estimate of roughness.
The set of signals R is sensed via a set of sensors 30 and comprises signals representative of said industrial process that vary over time, such as the high-dynamic and low-dynamic signals.
It may then be moreover envisaged to provide the signal Q processed by the pattern-recognition stage 26 to the interface unit 21 or other processing module, for example to control processing of the industrial process. Based on the pattern-recognition signal, for example the classification signal supplied to the processing machine, the above interface unit 21, which may be an autonomous processing module or a control sub-module in the processor module 20, is configured to carry out automatically operations such as warnings and/or alarms and/or rejection of pieces produced. Such operations or part of them may also be carried out manually.
The method specifically comprises performing an operation of pattern recognition by representing said at least one sensed signal R1, R2, R3 applied to which is said pattern-recognition operation 26 via a first digital image Rhf. In particular, it is envisaged to represent signals among the sensed signals R1, R2, R3 by applying a respective representation 242, 244, 246 of a set of representations 24 based on membership of the signals among the sensed signals R1, R2, R3 in a respective subset Rl, Rh, Rc defined in the aforesaid set of sensed signals R, producing corresponding digital images Rhf, Rlf, Rcf that represent said sensed signals R1, R2, R3. Hence, this operation is applied to one of the signals, R1 in the example, to which the pattern-recognition operation 26 is applied, whereas other signals of the set of sensed signals can then be chosen—based on their features, in particular slowly varying as compared to the signal R1 or constant—for the other representation operations 244, 246. In particular, at least one first representation 242 of the set of representations 24 comprises representing signals of a subset R1—for example, one of the signals, R1 in the example, to which the pattern-recognition operation 26 is applied and/or a high-dynamic signal, among the subsets Rl, Rh, Rc—that in particular comprises signals that vary in time, namely in the example high-dynamic signals, in an observation time window, for example the window K, via a map, in which one of the dimensions represented is time, and producing a corresponding first digital image Rhf of said set of digital images Rhf, Rlf, Rcf, which is the map, in particular a time-frequency map or a time-scale map.
Then, the method comprises producing at least one composite image Rf by superimposing 248 on the first digital image Rhf one or more digital images Rhf, Rcf, Rlf produced by signals of other subsets, so that, for example, to the image Rhf in the form of map graphic elements are added obtained via the other representations 244, 246 that improve operation of the pattern-recognition module 26.
The method then envisages that said pattern-recognition operation 26 is carried out via a pattern-recognition stage 26 comprising a recognition model trained on a set of said composite images Rf stored SV in a training dataset. Hence, in general it is envisaged that pattern recognition, which operates via a recognition model that carries out a classification or a regression, will use training datasets that also comprise composite images.
Finally, the method comprises applying said pattern-recognition operation 26 to an image that comprises said at least one composite image Rf, obtaining as a result at least one recognition signal Q indicative of a property of said industrial process, for example carrying out a classification of the discrete state, such as the quality class of the industrial process, or calculating via regression a continuous value such as the estimate of a measurement of a quantity of the process or of the product, for example an estimate of roughness.
The method described moreover envisages that the pattern-recognition operation 26 will comprise artificial-convolutional-neural-network (CNN) processing.
The method described moreover envisages that the aforesaid CNN processing 26 is trained on a set of composite images, for example individual images or images arranged in the form of a matrix, stored in a training dataset, for example in the server SV.
The method further comprises producing a plurality of composite images Rf1, . . . , Rf9 and arranging said plurality of composite images in an overall single digital composite image, in particular by setting them adjacent to one another according to a grid or matrix arrangement, and then applying said pattern-recognition operation 26 to said overall composite image Rf, obtaining as a result said at least one recognition signal Q indicative of a property of the industrial process.
The method further envisages that said plurality of composite images Rf1, . . . , Rf9 will comprise composite images whose first digital images Rhf are obtained from sensed signals R1 coming from different sensors 30.
According to the method described, the pattern-recognition operation 26 is a classification operation, and the property of said industrial process is a class of said industrial process, in particular a processing-quality class. In this context, the aforesaid training dataset comprises composite images associated to corresponding classes, in particular processing-quality classes.
According to the method described, the pattern-recognition operation 26 is an operation of regression, and the property of said industrial process is a value representative of said industrial process, in particular an estimate of a measurement made on the industrial process or its product. In this context, the training dataset comprises composite images associated to values of measurements made on the industrial process or its product.
According to the method described, at least one first representation 242 of set of representations 24 comprises representing signals of a subset R1—for example one of the signals, R1 in the example, to which the pattern-recognition operation 26 is applied and/or a high-dynamic signal, among the subsets Rl, Rh, Rc—that in particular comprises signals that vary in time, namely in the example high-dynamic signals, in an observation time window, for example the window K, via a map, in which one of the dimensions represented is time, and producing a corresponding first digital image Rhf of said set of digital images Rhf, Rlf, Rcf, which is the map, in particular a time-frequency map or a time-scale map.
Then, the method comprises in particular producing at least one composite image Rf by superimposing 248 on the first digital image Rhf one or more digital images Rhf, Rcf, Rlf produced by signals of other subsets, so that to the image Rhf in the form of map graphic elements are added via the other representations 244, 246 that improve operation of the pattern-recognition module 26, and applying to the at least one composite image Rf the pattern-recognition operation 26, obtaining at least one recognition signal Q indicative of a state of said industrial process or of a value/state of the product of processing as a result of said pattern-recognition operation 26.
The method further comprises determining the membership of the signals among the sensed signals R1, R2, R3 in a respective subset defined in said set of sensed signals R by assigning, for example via sorting 240 or other routing operation, signals among the sensed signals R1, R2, R3, into respective subsets of said set of sensed signals R; namely in the set of sensed signals R, subsets Rl, Rh, Rc are defined. In particular, the assignment is carried out via criteria of distinction, for example criteria of distinction based on the rapidity of temporal variation of the signal in the observation window, namely high dynamics or low dynamics (or constant dynamics), the low-dynamic signals being, for example, signals that have a prevalent frequency content with low frequency and possibly a limited bandwidth in the observation window.
In addition, the method envisages that the aforesaid map, in which one of the dimensions is time, will be obtained via a transform from the time domain to a two-dimensional domain in which one of the dimensions is time. In particular, said transform comprises at least one between a short-term Fourier transform (STFT) and a continuous-wavelet transform (CWT).
The operation of applying respective representations of the set of respective representations 242, 244, 246 to signals R1, R2, R3 of the set of sensed signals R so as to produce a respective digital image Rhf, Rlf, Rcf also comprises representing 244, 246 at least one second signal R2, R3 of said set of signals R by extracting a representative value over a time interval equal to or shorter than the time window of the first signal R1 and producing at least one second digital image Rlf, Rcf of said set of digital images Rhf, Rlf, Rcf to obtain at least one composite image Rf by superimposing 248 on said first digital image Rhf at least said second digital image Rcf, Rlf. In other words, it is envisaged to superimpose graphic elements that represent a value extracted from the signal with low variation in the window 244 or from the constant signals, for example, state parameters, or machine parameters, where the extracted value is the constant value itself understood as numeric value or as value of the information.
According to preferred embodiments, the extraction operation comprises computing a value, in particular an average value of the signal with low variation, and/or acquiring a value of process parameter. Preferably, there is present both a representation, in particular via an indicator or marker, of the extracted value of the signal with low variation and a representation via icons that represent the value or the corresponding information of one or more process parameters.
Without prejudice to the underlying principles, the details and the embodiments may vary, even appreciably, with respect to what has been described herein purely by way of example, without thereby departing from the sphere of protection and scope of the invention, as defined by the annexed claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 102020000031103 | Dec 2020 | IT | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/IB2021/061756 | 12/15/2021 | WO |
