METHOD AND DEVICE FOR DEMONSTRATING THE INFLUENCE OF CUTTING PARAMETERS ON A CUT EDGE

A method for recognizing cutting parameters which are particularly important for specific features of a cut edge. A recording of the cut edge is analyzed by an algorithm having a neural network for determining the cutting parameters. Those recording pixels which play a significant part for ascertaining the cutting parameters are identified by backpropagation of this analysis. An output in the form of a representation of these significant recording pixels, in particular in the form of a heat map, demonstrates to a user of the method which cutting parameters need to be changed in order to improve the cut edge. A computer program product and a device for carrying out the method.

FIELD AND BACKGROUND OF THE INVENTION

The invention relates to a method for analyzing a cut edge created by a machine tool. The invention furthermore relates to a computer program product and a device for carrying out the method.

It is known to optimize the cutting of workpieces. By way of example, DE 10 2017 105 224 A1 discloses the use of a neural network in order to regulate a laser cutting process.

However, most cutting processes or the influences of cutting parameters on the cut edges are not fully understood. This can be inferred from the following articles, for example:Hügel, H., Graf, T. Laser in der Fertigung: Strahlquellen, Systeme, Fertigungsverfahren [Lasers in manufacturing: beam sources, systems, manufacturing methods]. Wiesbaden: Vieweg+Teubner, 2009.Petring, D., Schneider, F., Wolf, N. Some answers to frequently asked questions and open issues of laser beam cutting. In: International Congress on Applications of Lasers & Electro-Optics. ICALEOR 2012, Anaheim, Calif., USA: Laser Institute of America, 2012, pages 43-48Steen, W. M., Mazumder, J. Laser Material Processing. London: Springer London, 2010.

Even experienced users of the cutting apparatuses generally cannot predict how the cutting parameters will affect the appearance of the cut edge. In order to improve the appearance of a cut edge, in particular in the case of problems on account of changed material quality and/or in the case of new processes using a new laser source, new sheet metal thickness, etc., it is therefore necessary regularly to carry out complicated test series with cutting parameters varied in different ways.

SUMMARY OF THE INVENTION

Against this background, it is an object of the invention to provide a method, a computer program product and a device which are able to analyze the influence of the cutting parameters on the cut edge.

DESCRIPTION OF THE INVENTION

This object is achieved according to the invention by a method as claimed, a computer program product as claimed, and a device as claimed. The dependent claims present preferred developments.

The object according to the invention is thus achieved by a method in which a recording of a cut edge created by a machine tool, said recording having a plurality of recording pixels, is read in. An algorithm having a neural network is used to determine at least one cutting parameter, in particular a plurality of cutting parameters, from the recording. This is followed by a backpropagation in the neural network to ascertain the relevance of the individual recording pixels for ascertaining the previously determined cutting parameters. The recording is then output with at least some recording pixels being marked, the marking reflecting the previously ascertained relevance of the recording pixels. Preferably, all the recording pixels are output and marked in accordance with their relevance.

Consequently, a user immediately discerns from the marked output the extent to which the respective regions of the cut edge were influenced by the respective cutting parameter or the respective cutting parameters, and can then perform an adaptation of said cutting parameter or said cutting parameters in order to change a specific region of the cut edge in a targeted manner.

The backpropagation of a neural network was disclosed by EP 3 654 248 A1, for example, the content of which is incorporated by reference in its entirety in the present description.

Such a backpropagation (“backpropagation-based mechanisms”) is usually used only to check whether a neural network has learned the correct relationships. In this context, there are neural networks which do not have “superhuman performance”. In this case, a human has no problems in being able to assess what the correct information is. For example, it is thus possible to examine whether a neural network which can differentiate dogs from cats actually recognizes a dog in an image when it indicates the presence of a dog, and is not considering the meadow in which the dog is standing. By way of example, it can happen that instead of recognizing a specific animal (e.g. a horse) in an image, the neural network recognizes lettering in the image that can be seen in all images of horses (so-called “Clever Hans problem”). In the present case, by contrast, backpropagation is used in order to understand or at least be able to predict production processes or physical relationships during the cutting process.

In this case, a neural network is understood to mean an architecture having at least one data aggregation routine, in particular a plurality of data aggregation routines. A data aggregation routine can be designed to aggregate a plurality of “determined data” to form a new data packet. The new data packet can comprise one or more numbers or vectors. Further data aggregation routines can be made available to the new data packet fully or in part as “determined data”. “Determined data” can be in particular cutting parameters or data packets made available by one of the data aggregation routines. Particularly preferably, the architecture is configured with a plurality of connected data aggregation routines. In particular, hundreds, in particular thousands, of such data aggregation routines can be connected to one another. The quality of the neural network is significantly improved as a result.

In this case, the architecture can have a function with weighted variables. One data aggregation routine, in particular a plurality of data aggregation routines, particularly preferably all data aggregation routines, can be designed to combine, in particular to multiply, each of a plurality of “determined data” with, or by, a weighted variable and thus to convert the “determined data” into “combined data” so as then to aggregate, in particular to add, the “combined data” to form a new data packet. In the neural network, data can be multiplied by weights. The information of a plurality of neurons can be added. Furthermore, the neural network can have a nonlinear activation function.

The cut edge features contained in the recording can in this case themselves be data packets, in particular a plurality of structured data, in particular data vectors or data arrays, which can themselves again constitute “determined data”, in particular for the data aggregation routines.

For determining suitable weighted variables, i.e. for training the neural network, it is possible to run through the method with data, in particular cutting parameters, whose association with recordings is known in each case.

The neural network here is preferably configured in the form of a convolutional neural network (CNN) having a plurality of layers. The convolutional neural network can have convolutional layers and pooling layers. Pooling layers are typically arranged between two successive convolutional layers. As an alternative or in addition thereto, a pooling can be carried out after each convolution.

In addition to the convolutional layers and pooling layers, a CNN can have fully connected layers, in particular right at the end of the neural network. The convolutional layers and pooling layers extract features, and the fully connected layers can assign the features to the cutting parameters.

The neural network can have a plurality of filters per layer. The structure of a convolutional neural network can be gathered for example from the following articles, in particular the first one mentioned below:LeCun Y, Bengio Y, Hinton G (2015) Deep learning; Nature 521:436{444, DOI 10.1038/nature14539;Lin H, Li B, Wang X, Shu Y, Niu S (2019); Automated defect inspection of LED chip using deep convolutional neural network; J Intell Manuf; 30:2525{2534, DOI 10.1007/s10845-018-1415-x;Fu G, Sun P, Zhu W, Yang J, Cao Y, Yang M Y, Cao Y (2019); A deep-learning-based approach for fast and robust steel surface defects classification; Opt Laser Eng 121:397{405, DOI 10.1016/j.optlaseng.2019.05.005;Lee K B, Cheon S, Kim C O (2017) A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes; IEEE T Semiconduct M 30:135{142, DOI 10.1109/TSM.2017.2676245;Gonçalves D A, Stemmer M R, Pereira M (2020) A convolutional neural network approach on bead geometry estimation for a laser cladding system; Int J Adv Manuf Tech 106:1811{1821, DOI 10.1007/s00170-019-04669-z;Karatas A, Kölsch D, Schmidt S, Eier M, Seewig J (2019) Development of a convolutional autoencoder using deep neuronal networks for defect detection and generating ideal references for cutting edges; Munich, Germany, DOI 10.1117/12.2525882;Stahl J, Jauch C (2019) Quick roughness evaluation of cut edges using a convolutional neural network; In: Proceedings SPIE 11172, Munich, Germany, DOI 10.1117/12.2519440.

Backpropagation, in particular in the form of layer-wise relevance propagation, is preferably based on deep Taylor decomposition (DTD). Deep Taylor decomposition can be gathered in particular from the following article:Montavon G, Lapuschkin S, Binder A, Samek W, Müller K R (2017) Explaining NonLinear Classification Decisions with Deep Taylor Decomposition; Pattern Recognition 65:211{222, DOI 10.1016/j.patcog.2016.11.008.

With further preference, the outputting is effected in the form of a heat map. The heat map can have two colors, in particular red and blue, which respectively identify particularly relevant and particularly irrelevant recording pixels. Recording pixels of average relevance can be identified by intermediate shades between the two colors or gray. As a result, the output is understandable particularly intuitively.

The recording is preferably a photograph, particularly preferably a color photograph, in particular in the form of an RGB photograph, or a3D point cloud.3D point clouds are somewhat more complicated to create since they include depth information. The depth information can be obtained during the creation of the recording in particular by means of light section or by means of triangulation from different angles. However, it has been found that color photographs are particularly suitable or sufficient since the various cutting parameters are recognized by the neural network primarily from the different colorations of the cut edge.

The recording can be created by a photographic and/or video camera. Preferably, the camera is part of the machine tool in order to ensure a constant recording situation. As an alternative or in addition thereto, the camera can be part of a photo booth in order to reduce ambient influences during the creation of the recording.

With further preference, the method according to the invention comprises creating the cut edge by means of the machine tool. The cutting method of the machine tool can be a thermal cutting method, in particular a plasma cutting method, preferably a laser cutting method.

The cutting parameters determined in the case of a laser cutting method preferably comprise beam parameters, in particular focus diameter and/or laser power; transport parameters, in particular focus position, nozzle-focus distance and/or feed; gas dynamics parameters, in particular gas pressure and/or nozzle-workpiece distance; and/or material parameters, in particular degree of gas purity and/or melting point of the workpiece. These cutting parameters have proved to be particularly formative for the appearance of the cut edge.

The object according to the invention is furthermore achieved by a computer program product for carrying out the computation operations described here. The computer program product can be configured in partly, in particular fully, cloud-based fashion in order to enable a plurality of users to have access to the computer program product. Furthermore, more comprehensive training of the neural network can be effected by a plurality of users.

Finally, the object according to the invention is achieved by a device for carrying out the method described here, wherein the device comprises the machine tool, in particular in the form of a laser cutting machine.

In this case, the device can comprise the camera described here.

Further advantages of the invention are evident from the description and the drawing. Likewise, according to the invention, the features mentioned above and those that will be explained still further can be used in each case individually by themselves or as a plurality in any desired combinations. The embodiments shown and described should not be understood as an exhaustive enumeration, but rather are of exemplary character for outlining the invention.

Although the invention is illustrated and described herein as embodied in a method and device for demonstrating the influence of cutting parameters on a cut edge, it is nevertheless not intended to be limited to the details shown, since various modifications and structural changes may be made therein without departing from the spirit of the invention and within the scope and range of equivalents of the claims.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1shows part of a machine tool10in the form of a laser cutting machine. In this case, a cutting head12passes over a workpiece14with the workpiece14being subjected to laser irradiation and exposure to gas. A cut edge16is produced in the process. The cut edge16is influenced in particular by the following cutting parameters18: gas pressure20, feed22, nozzle-workpiece distance24, nozzle-focus distance26and/or focus position28.

The influence of the individual cutting parameters18on the appearance of the cut edge16obtained is to a very great extent unclear even to experts. If striation occurs on the cut edge16, for example, the cutting parameters18must be varied until the striation disappears, in which case, firstly, the variation is associated with high consumption of material and energy and expenditure of time and, secondly, it often happens that new artefacts are produced by the variation. There is therefore the need to provide a method and a device by which cutting parameters18are assigned to the features of a cut edge16in a targeted manner. These cutting parameters18can then be changed in order to change the feature of the cut edge16. The invention therefore solves a problem which cannot be solved by human users on account of the complexity of the problem (“superhuman performance”).

FIG. 2shows an overview of the method according to the invention. In method step A), the cut edge16is produced by the machine tool10using the cutting parameters18. In method step B), the cut edge16(see method step A)) is recorded using a camera30. The camera30can be configured in the form of a photographic camera and/or a video camera. In method step C), the created recording32is read in. In method step D), the recording32is analyzed by an algorithm34. The algorithm34has a neural network36. The neural network36serves for determining38the cutting parameters18. The determined cutting parameters18can be compared with the set cutting parameters18(see method step A)), for example in order to determine a defect of the machine tool10(see method step A)).

In method step E), the algorithm34effects a backpropagation40in the neural network36. The backpropagation40of the cutting parameters18with respect to the recording32establishes the relevance of individual recording pixels42a,42bof the recording40when determining the cutting parameters18in method step D). In method step F), the recording pixels42a, bare represented (only the recording pixels42a, bbeing provided with a reference sign inFIG. 2, for reasons of clarity) and their respective relevance is identified. In the present case, the particularly relevant recording pixel42ais identified using a first color (for example red) and the particularly irrelevant recording pixel42bis identified using a second color (for example blue or gray). Owing to formal stipulations, the different colors are represented by different patterns (hatchings) in the present description. On the basis of the particularly relevant recording pixels42a, a user can directly recognize which regions of the recorded cut edge16(see method step A)) are particularly influenced by the cutting parameter18respectively determined (see method step D)).

FIG. 3shows by way of example three recordings32a,32b,32c, the recordings32a-chaving been created with different cutting parameters18(seeFIG. 1):

By comparison therewith, recording32bwas created with an increased nozzle-focus distance26. Recording32cwas created with a reduced feed22by comparison with recording32a. It is evident fromFIG. 3that the influence of the cutting parameters18(seeFIG. 1) is not directly inferable from the recordings32a-cfor human users.

FIG. 4schematically shows the algorithm34or more specifically the neural network36. The neural network36is constructed in the form of a convolutional neural network having a plurality of blocks44a,44b,44c,44d,44e. In this case, an input block44ais provided. The blocks44b-eeach have three convolutional layers46a,46b,46c,46d,46e,46f,46g,46h,46i,46j,46k,46l. The blocks44a-ehave filters48a,48b,48c,48d,48e. Each layer of the input block44ahas 32 filters48a. The layers of the block44blikewise have 32 filters48b. The layers of the block44chave 64 filters48c. The layers of the block44dhave 128 filters48dand the layers of the block44ehave 256 filters48e. The filters48a-ecan result in a reduction of the resolution of the recording32(e.g. from 200 pixels×200 pixels to 7 pixels×7 pixels) with at the same time an increase in the depth (or number of channels). The filters48a-eof the third layer of each block44a-eresult in a reduction of the resolution. Convolutional layers are used here for the pooling as well. The depth increases from one block44a-eto the next. By way of example, the block44bconsists of three convolutional layers, each having 32 filters. In the first two, the spatial resolution is 112×112 pixels. From the second to the third, the spatial resolution decreases from 112×112 pixels to 56×56 pixels. Upon the transition from block44b(last layer) to block44c(first layer), the depth increases from 32 to 64. The spatial resolution remains constant.

The neural network36thereby enables determining38of the cutting parameters18. In the present case, layer-wise relevance propagation is used in the backpropagation40. The results of this are illustrated inFIG. 5.

FIG. 5shows the recording32ain the upper column and the recording32bin the lower column. The recordings32a, bare reproduced a number of times in each column, the recording pixels42a, binfluenced greatly or little by the feed22being highlighted in the second column, the recording pixels42a, binfluenced greatly or little by the focus position28being highlighted in the third column, and the recording pixels42a, binfluenced greatly or little by the gas pressure20being highlighted in the fourth column. In this case, the outputs50can be present in the form of heat maps.

The recording pixels42binfluenced particularly little by the respective cutting parameter18(seeFIG. 1) serve primarily for checking the plausibility of the outputs50. Preferably, in the outputs50, only the recording pixels42ainfluenced particularly greatly by the respective cutting parameter18(seeFIG. 1) are highlighted in order to facilitate handling of the outputs50for a user.

Taking all the figures of the drawing jointly into consideration, the invention relates in summary to a method for recognizing cutting parameters18which are particularly important for specific features of a cut edge16. In this case, a recording32,32a-cof the cut edge16is analyzed by an algorithm34having a neural network36for determining38the cutting parameters18. Those recording pixels42a, bwhich play a significant part for ascertaining the cutting parameters18are identified by backpropagation40of this analysis. An output50in the form of a representation of these significant recording pixels42a, b, in particular in the form of a heat map, demonstrates to a user of the method which cutting parameters18need to be changed in order to improve the cut edge16. The invention furthermore relates to a computer program product and respectively a device for carrying out the method.

The following is a summary list of reference numerals and the corresponding structure used in the above description of the invention:10Machine tool12Cutting head14Workpiece16Cut edge18Cutting parameters20Gas pressure22Feed24Nozzle-workpiece distance26Nozzle-focus distance28Focus position30Camera32,32a-cRecording34Algorithm36Neural network38Determining the cutting parameters1840Backpropagation42a, bRecording pixels44a-eBlocks of the neural network3646a-lLayers of the neural network3648a-eFilters of the neural network3650Output