Method for replacing a set of cutting parameters for producing a cutting edge by means of a laser cutting machine with an optimised set of cutting parameters, and laser cutting machine

Abstract

The invention relates to a method for replacing a set of cutting parameters for producing a cutting edge (12) by means of a laser cutting machine (10) with an optimised set of cutting parameters, the method comprising the following steps: a1) producing the cutting edge (12) by means of the laser cutting machine (10) using the set of cutting parameters, wherein the cutting edge has a machining error (14), b) classifying the machining error (14) according to the type of machining error (14), c) generating several modified sets of cutting parameters based on the classification, d) producing further cutting edges (16) by means of the laser cutting machine (10) using the modified sets of cutting parameters, wherein each modified set of cutting parameters is used to produce one further cutting edge (16), e) creating an evaluation (18) of the further cutting edges (16) by rating an occurrence of machining errors (22) for each of the further cutting edges (16), f1) ascertaining a further cutting edge (16) evaluated as best in the evaluation (18) of the further cutting edges (16), g) replacing the set of cutting parameters with the optimised set of cutting parameters, wherein the optimised set of cutting parameters is based on the modified set of cutting parameters with which the ascertained further cutting edge was produced.

Description

[0001] Title : Method for replacing a set of

[0002] Cutting parameters for producing a cutting edge with a laser cutting machine through an optimized set of cutting parameters and laser cutting machine

[0003] Description

[0004] The invention relates to a method for replacing a set of cutting parameters for producing a cutting edge with a laser cutting machine by an optimized set of cutting parameters and a laser cutting machine.

[0005] Typically, a set of cutting parameters is used to produce a cut edge with a laser cutting machine. The set of cutting parameters may also be referred to as a set of setup parameters, a set of device setup parameters, or a set of scattering parameters, especially for the laser cutting machine used to produce the cut edge. Producing the cut edge may also be referred to as cutting the workpiece.

[0006] The set of cutting parameters typically includes adjustment parameters for setting the laser machine, which in particular have a direct influence on the quality of the produced cutting edge.

[0007] The set of cutting parameters, and in particular their values, often depends on the properties of the workpiece. The properties of the workpiece can be, for example, a material and / or a thickness of the workpiece. With a suitable selection of the set of cutting parameters, and in particular their values, the laser cutting machine can produce a cut edge that is completely or almost free of machining defects. With an unsuitable selection of the set of cutting parameters, and in particular their values, machining defects, such as a burr or roughening of the cut edge, can occur in such a way that the produced cut edge must be reworked or the workpiece is unusable.

[0008] DE 10 2019 127 323 A1 discloses a laser processing system for carrying out a processing process on a workpiece by means of a laser beam. The laser processing system comprises a sensor unit for monitoring the processing process. The laser processing system comprises a computing unit which outputs control data to a control unit of the laser processing system in order to optimize the processing process in each state by means of a corresponding control action and to maintain it in the optimized state. The object of the invention is to provide a method for replacing a set of cutting parameters which enables cost-effective production of cut edges. Furthermore, it is the object of the present invention to provide a laser cutting machine which is designed to carry out the method.

[0009] The invention solves this problem by providing a method having the features of claim 1 and a laser cutting machine having the features of claim 11. Advantageous developments and / or refinements of the invention are described in the dependent claims.

[0010] A method according to the invention is designed to replace a set of cutting parameters for producing a cutting edge with a laser cutting machine with an optimized set of cutting parameters. The method comprises the steps: a) producing the cutting edge with the laser cutting machine using the set of cutting parameters, wherein the cutting edge has a machining error, b) classifying the machining error according to a type of machining error, c) generating several, in particular 5 or 10, modified sets of cutting parameters based on the classification, d) producing further cutting edges with the laser cutting machine using the modified sets of cutting parameters, wherein each modified set of cutting parameters is used for producing a further cutting edge,e) Creating an evaluation of the further cutting edges by evaluating the occurrence of machining errors for each of the further cutting edges, fl) Determining a further cutting edge that is best rated in the evaluation of the further cutting edges, g) Replacing the set of cutting parameters with the optimized set of cutting parameters, wherein the optimized set of cutting parameters is based on the modified set of cutting parameters with which the further cutting edge determined, in particular in step fl), was produced.

[0011] Advantageously, the method is executed as needed, particularly when a machining error occurs. Therefore, no large amounts of data are generated when cutting edges are produced, and no powerful computing resources are required for cutting edges. This allows cutting edges to be produced using cost-effective computing resources. Therefore, the method enables cost-effective cutting edges.

[0012] The set of cutting parameters can have a number, in particular 1 to 10, of cutting parameters. The optimized set of cutting parameters can have a number, in particular 1 to 10, of cutting parameters. The modified set of cutting parameters can have a number, in particular 1 to 10, of cutting parameters. The number of cutting parameters of the set of cutting parameters, the number of cutting parameters of the optimized set of cutting parameters and the number of cutting parameters of the modified set of cutting parameters can be the same.

[0013] The set of cutting parameters may be a subset of a set of adjustment parameters of the laser cutting machine. A cutting edge produced with the optimized set of cutting parameters may have no machining error or a reduced machining error compared to a cutting edge produced with the set of cutting parameters.

[0014] The optimized set of cutting parameters may differ from the set of cutting parameters in at least one value of a cutting parameter. For example, a value of a cutting parameter of the optimized set of cutting parameters may be smaller or larger than a value of the cutting parameter of the set of cutting parameters.

[0015] The classification of the processing error can be performed by a user.

[0016] Classification can be referred to as classifying, categorizing, assigning, or determining. The type of machining error can be referred to as the genus of the machining error. A plurality of classifiable types of machining errors can be specified, with the classification of the machining error being a selection of one type of machining error from the specified plurality of classifiable types of machining errors.

[0017] If the cutting edge has several different types of machining errors, the classification of the machining error can be a classification of the dominant machining error according to a type of the dominant machining error. The generation of the modified sets of cutting parameters can comprise a modification of the set of cutting parameters. In other words, each modified set of cutting parameters can be obtained, in particular acquired, by means of a modification of the set of cutting parameters. The modification can take place as a function of the classification of the machining error, in particular of the classified type of machining error. The classification of the machining error, in particular the classified type of machining error, can define at least one cutting parameter of the set of cutting parameters to be modified. The modification can be referred to as a change.

[0018] The plurality of modified sets of cutting parameters may differ from each other, in particular in a number of cutting parameters and / or in a value of the cutting parameters.

[0019] The evaluation may include the assessment of the occurrence of machining defects on each of the other cutting edges. The assessment of the occurrence of machining defects may be an assessment of the severity and / or frequency of the machining defects.

[0020] For example, another cutting edge may receive a poor rating due to a severe and / or frequently occurring machining error. For example, another cutting edge may receive a good rating due to a mild and / or minor machining error. The best-rated another cutting edge may not have any machining error. Alternatively, the best-rated another cutting edge may be the one with the least severe and / or least frequently occurring machining error.

[0021] In a further development of the method, the classification of step b) is a computer-implemented classification. The classification can be carried out by a control device, in particular the laser cutting machine. The control device can have, in particular be, a computing device, a control unit, a microcontroller and / or a computer. The classification can be carried out locally, in particular by the laser cutting machine or by a local server, or by means of cloud computing.

[0022] In a further development of the method, the method comprises the following step after step a1): a2) transmitting a material of a workpiece having the cutting edge. The classification of step b) is carried out based on the transmitted material.

[0023] The method may comprise the steps of: a0) specifying a number of types of machining errors and a3) limiting the number of types of machining errors based on the supplied material. The limited number of types of machining errors may include those types of machining errors that may occur when producing a cut edge in the supplied material using a laser cutting machine. The limited number of types of machining errors may not include those types of machining errors that may not occur when producing a cut edge in the supplied material using a laser cutting machine.

[0024] The classification in step b) based on the submitted material may mean that, in particular, only a limited number of types of processing errors are available for classification. In other words, the type of processing error classified in step b) may, in particular, only be one type of processing error from the limited number of types of processing errors.

[0025] The material of the workpiece can be mild steel, stainless steel, aluminum, copper or brass.

[0026] If the classification of the machining error is a computer-implemented classification, the transmission of the material of step a2) can be a transmission to the control device. If the classification of the machining error is carried out by a user, the transmission of the material of step a2) can be a transmission to the user. If the classification of the machining error is carried out by a user, alternatively or additionally, the transmission of the material of step a2) can be a transmission to a display for displaying the limited number of types of machining errors of step a3), wherein the display is configured to carry out step a3).

[0027] In a further development of the method, the cutting edge of step a1) is produced using a process gas. After step a1), the method comprises the step: a6) transmitting the process gas used in producing the cutting edge. The classification of step b) is carried out based on the transmitted process gas.

[0028] The method may comprise the steps of: a0) specifying a number of types of machining errors and a7) limiting the number of types of machining errors based on the transmitted process gas. The limited number of types of machining errors may include those types of machining errors that may occur when producing a cut edge with a laser cutting machine using the transmitted process gas. The limited number of types of machining errors may not include those types of machining errors that may not occur when producing a cut edge with a laser cutting machine using the transmitted process gas.

[0029] The classification in step b) based on the transmitted process gas may mean that, in particular, only a limited number of types of machining defects are available for classification. In other words, the type of machining defect classified in step b) may, in particular, only be one type of machining defect from the limited number of types of machining defects.

[0030] The process gas can be nitrogen or oxygen.

[0031] If the classification of the machining error is a computer-implemented classification, the transmission of the process gas of step a6) can be a transmission to the control device. If the classification of the machining error is carried out by a user, the transmission of the process gas of step a6) can be a transmission to the user. If the classification of the machining error is carried out by a user, alternatively or additionally the transmission of the process gas of step a6) can be a transmission to a display for displaying the limited number of types of machining errors of step a7), wherein the display is configured to carry out step a7).

[0032] In a further development of the method, the method comprises the following step after step a1): a4) creating digital image data of the machining defect using a camera, in particular a digital one, and / or a5) uploading digital image data of the machining defect. The classification of step b) is carried out based on the digital image data.

[0033] In a further development of the method, the classification of step b) is carried out by means of an image recognition algorithm, an image comparison algorithm and / or a machine learning algorithm. The image recognition rhythm can comprise, in particular be, a feature extraction and / or feature reduction. The machine learning algorithm can comprise, in particular be a trained, neural network. In particular, the image recognition algorithm, the image comparison algorithm and / or the machine learning algorithm can classify the processing error by means of an analysis of the digital image data. In a further development of the method, the evaluation of step e) is a computer-implemented evaluation or an evaluation by a user.

[0034] If the evaluation is performed by a user, creating the evaluation of the additional cutting edges of step e) may comprise displaying a rating scale for each additional cutting edge and recording the rating using the rating scale for each additional cutting edge. The evaluation may comprise the user entering the rating into the rating scale for each additional cutting edge.

[0035] If the evaluation is a computer-implemented evaluation, creating the evaluation of the further cutting edges of step e) can comprise the steps of: creating digital image data of each further cutting edge with a camera and values ​​of the occurrence of processing errors for each of the further cutting edges by means of an analysis of the digital image data, in particular a computer-implemented analysis. The analysis of the digital image data can comprise an application of an image recognition algorithm, an image comparison algorithm and / or a machine learning algorithm, in particular to the digital image data. The image recognition rhythm can comprise, in particular be, a feature extraction and / or feature reduction. The machine learning algorithm can comprise, in particular be, a neural network, in particular a trained one.

[0036] In a further development of the method, the type of machining defect of the cutting edge is a burr, a jet break, a melt tipping over, a slag adhesion, a wavy cut start, a groove trailing edge, a roughening, a pitting, a spontaneous combustion, a slagging, a welding of the cutting edge, a cut surface discoloration, a cut edge discoloration, a corner discoloration, a corner discoloration and / or a cut end discoloration.

[0037] In a further development of the method, if a single further cutting edge is determined in step f1), the optimized set of cutting parameters is equal to the modified set of cutting parameters with which the determined further cutting edge was produced. Alternatively, if a plurality of further cutting edges are determined in step f1), the method comprises the step after step f2): creating the optimized set of cutting parameters by calculating the mean value of the modified sets of cutting parameters with which the plurality of determined further cutting edges were produced.

[0038] In a further development of the method, the generation of several modified sets of cutting parameters of step c) is carried out by applying a predetermined optimization rule to the set of cutting parameters.

[0039] The predetermined optimization rule may be a rule how the set of cutting parameters, in particular its values ​​and / or its cutting parameters, are to be changed in order to generate the plurality of modified sets of cutting parameters.

[0040] The type of machining error classified in step b) can be assigned the predefined optimization rule. For example, the type of machining error classified in step b) can be a jet break, spontaneous combustion, or roughening, and the set of cutting parameters can include a feed rate. The predefined optimization rule assigned to the jet break, spontaneous combustion, or roughening can include a reduction of a feed rate value, for example, by 4%, 6%, 8%, 10%, 12%, and 14%, so that a number, in particular six, of modified sets of cutting parameters are generated by reducing the feed rate value of the set of cutting parameters.

[0041] For example, the type of machining defect classified in step b) may be a melt tipping or slag adhesion, and the set of cutting parameters may have a focal position, wherein the predetermined optimization rule associated with the melt tipping or slag adhesion may include a change in a value of the focal position, for example by +2 mm, +1.5 mm, +1 mm, +0.5 mm, -0.5 mm, -1 mm, -1.5 mm, and -2 mm, so that a number, in particular eight, of modified sets of cutting parameters are generated by changing the value of the focal position of the set of cutting parameters. The abbreviation mm may denote the unit millimeter.

[0042] For example, the type of machining error classified in step b) may be a wavy cut start and the set of cutting parameters may include a nozzle-workpiece distance, wherein the predetermined optimization rule associated with the wavy cut start may include a change in a value of the nozzle-workpiece distance, for example by +0.5 mm, +0.3 mm, +0.15 mm, -0.15 mm, -0.3 mm and -0.5 mm, so that a number, in particular six, of modified sets of cutting parameters are generated by changing the value of the nozzle-workpiece distance of the set of cutting parameters.

[0043] For example, the type of machining defect classified in step b) may be a cut surface discoloration and the set of cutting parameters may comprise a gas pressure, wherein the predetermined optimization rule associated with the cut surface discoloration may comprise an increase in a value of the gas pressure, for example by +5 bar, +4 bar, +3 bar, +2 bar and +1 bar, so that a number, in particular five, of modified sets of cutting parameters are generated by increasing the value of the gas pressure of the set of cutting parameters.

[0044] For example, the type of machining defect classified in step b) may be cratering and the set of cutting parameters may comprise a gas pressure, wherein the predetermined optimization rule associated with the cratering may comprise a change in a value of the gas pressure, for example by +0.5 bar, +0.2 bar, +0.1 bar, -0.1 bar, -0.2 bar and -0.5 bar, so that a number, in particular six, of modified sets of cutting parameters are generated by changing the value of the gas pressure of the set of cutting parameters.

[0045] The specified optimization rule may include, in particular, a heuristic, an experience rule and / or a machine learning method.

[0046] The specified optimization rule may change a value of one cutting parameter of the set of cutting parameters and subsequently change a value of another cutting parameter of the set of cutting parameters.

[0047] The multiple modified sets of cutting parameters can be generated in a waterfall manner or interactively.

[0048] A number of optimization rules can be specified, with each optimization rule being assigned to one, in particular a single, type of machining error, preferably classifiable in step b). The number of optimization rules and a number of types of machining errors, in particular classifiable in step b), can be the same.

[0049] The generation of a plurality of modified sets of cutting parameters of step c) may comprise selecting the predetermined optimization rule associated with the type of machining error classified in step b).

[0050] In a further development of the method, the set of cutting parameters comprises at least one cutting parameter from a set of focus diameter, laser power, nozzle-focus distance, nozzle-workpiece distance, feed rate, gas pressure, nozzle diameter, and gas type. Generating a plurality of modified sets of cutting parameters in step c) comprises changing the at least one cutting parameter, in particular a value of the at least one cutting parameter.

[0051] A laser cutting machine according to the invention is designed to carry out a method as described above. The laser cutting machine can have a control device for carrying out the method as described above, in particular for classifying the machining error according to a type of machining error. The control device can have, in particular be, a computing device, a microcontroller, a computer, a production control system, a manufacturing execution system, a programmable logic controller, and / or an IPC-based controller.

[0052] Further advantages and advantageous embodiments of the invention can be found in the following drawings, their description, and the patent claims. All features disclosed in the drawings, their description, and the patent claims can be essential to the invention both individually and in any combination. They show:

[0053] Fig. 1 is a schematic flow diagram of a method for replacing a set of cutting parameters for producing a cutting edge with a laser cutting machine by an optimized set of cutting parameters,

[0054] Fig. 2 is a schematic view of a laser cutting machine designed to carry out a method for replacing a set of cutting parameters for producing a cutting edge with an optimized set of cutting parameters,

[0055] Fig. 3 is a schematic view of a cut edge produced with the laser cutting machine of Fig. 2, and Fig. 4 is a schematic view of further cut edges produced with the laser cutting machine of Fig. 2 and a schematic view of an evaluation of the further cut edges.

[0056] Fig. 1 shows a flow diagram of a method for replacing a set of cutting parameters for producing a cutting edge using a laser cutting machine with an optimized set of cutting parameters. The optimized set of cutting parameters differs from the set of cutting parameters at least in one value of a cutting parameter.

[0057] The method comprises the step: aO) specifying a number of types of machining defects. The specified types of machining defects can be a burr, a jet break, a melt tipping over, slag adhesion, a wavy cut start, a groove tail, roughening, cratering, spontaneous combustion, slagging, welding of the cut edge, cut surface discoloration, cut edge discoloration, corner discoloration, corner discoloration and / or cut end discoloration.

[0058] The method comprises the step of: a1) producing the cut edge with the laser cutting machine using the set of cutting parameters, wherein the cut edge has a machining error. The set of cutting parameters comprises a number of setting parameters of the laser cutting machine that are used to produce the cut edge. The method comprises the step of: a2) transmitting a material of a workpiece that has the cut edge. The laser cutting machine has a sensor for detecting the material. The sensor detects and transmits the material of the workpiece. Alternatively, the material of the workpiece can be transmitted by a user of the laser cutting machine.

[0059] In an alternative embodiment not shown, the cutting edge of step a1) is produced using a process gas and the method comprises the step after step a1): a6) transmitting the process gas used in producing the cutting edge.

[0060] The method comprises the step of: a3) limiting the number of types of machining defects based on the supplied material. The limited number of types of machining defects comprises those types of machining defects that can occur when producing a cutting edge in the supplied material with the laser cutting machine.

[0061] The method comprises the step: a4) Creating digital image data of the machining defect with a digital camera.

[0062] The method comprises the step: b) computer-implemented classification of the machining defect according to a type of machining defect based on the digital image data of the machining defect and based on the transmitted material. In an alternative embodiment not shown, the classification of step b) is carried out based on a transmitted process gas, if the process gas used in producing the cut edge was transmitted.

[0063] The classified type of processing error is not a type of processing error that is excluded based on the submitted material.

[0064] The classification is performed by a computer on the laser cutting machine. The classification is carried out using a machine learning algorithm. The machine learning algorithm is a trained neural network that classifies the type of machining error.

[0065] The method comprises the step of: c) generating a plurality of, in particular 5 or 10, modified sets of cutting parameters based on the classification. Each modified set of cutting parameters is obtained by changing the values ​​of the set of cutting parameters.

[0066] A predefined optimization rule is assigned to the classified type of machining error. The generation of a plurality of modified sets of cutting parameters in step c) is performed by applying the predefined optimization rule to the set of cutting parameters. The predefined optimization rule is a rule specifying how values ​​of the set of cutting parameters are to be changed in order to generate the plurality of modified sets of cutting parameters. The plurality of modified sets of cutting parameters differ from one another in at least one value of the cutting parameters.

[0067] The method comprises the step: d) producing further cut edges with the laser cutting machine using the modified sets of cutting parameters, wherein each modified set of cutting parameters is used for producing a further cut edge.

[0068] The method comprises the step: e) creating an evaluation of the further cutting edges by evaluating an occurrence of machining errors for each of the further cutting edges. The evaluation of the occurrence of machining errors is an evaluation of a severity and / or a frequency of the machining errors of each of the further cutting edges. The best-rated further cutting edge may not have any machining errors. Alternatively, the best-rated further cutting edge may be the further cutting edge with the least pronounced and / or least occurring machining error.

[0069] The evaluation is a computer-implemented evaluation. Creating the evaluation of the additional cutting edges involves creating digital image data of each additional cutting edge using a camera and evaluating the occurrence of machining errors for each of the additional cutting edges using a computer-implemented analysis of the digital image data. The computer-implemented analysis of the digital image data is an application of a machine learning algorithm to the digital image data. The machine learning algorithm is a trained neural network for evaluating the occurrence of machining errors.

[0070] The method comprises the step : fl ) determining a further cutting edge which is best evaluated in the evaluation of the further cutting edges .

[0071] If a single additional cutting edge is determined in step fl ), the optimized set of cutting parameters is equal to the modified set of cutting parameters with which the determined additional cutting edge was produced.

[0072] If a plurality of further cutting edges are determined in step fl), the method comprises the step after step fl): f2) Creating the optimized set of cutting parameters by calculating the mean value of the modified sets of cutting parameters with which the plurality of determined further cutting edges were produced. In particular, the values ​​of the cutting parameters of the optimized set of cutting parameters are equal to an average value of the values ​​of the cutting parameters of the modified sets of cutting parameters with which the plurality of determined further cutting edges were produced.

[0073] The method comprises the step: g) replacing the set of cutting parameters with the optimized set of cutting parameters, wherein the optimized set of cutting parameters is based on the modified set of cutting parameters with which the further cutting edge determined in step f1) was produced. Fig. 2 shows a laser cutting machine 10 which is designed to carry out a method for replacing a set of cutting parameters for producing a cutting edge with an optimized set of cutting parameters.

[0074] The laser cutting machine 10 has a camera 26, a control unit 28, a display 30, and a laser cutting head 32 with a nozzle 34. The control unit 28 is designed to control the laser cutting head 32 for producing a cutting edge 12 with the laser cutting machine 10.

[0075] The control unit 28 has a set of cutting parameters for producing the cutting edge. The set of cutting parameters includes the following cutting parameters: focus diameter, laser power, nozzle-to-focus distance 36, nozzle-to-workpiece distance 38, feed rate 40, gas pressure 42, nozzle diameter, and gas type.

[0076] Using the set of cutting parameters and additional setting parameters, the laser cutting machine 10 produces the cutting edge 12. Producing the cutting edge 12 involves cutting a workpiece 24. The workpiece 24 has the cutting edge 12.

[0077] Fig. 3 shows the workpiece 24 with the cut edge 12, which was produced with the laser machine 10 using the set of cutting parameters. The cut edge 12 has a machining defect 14 in the form of a burr. The machining defect 14 occurred due to an unsuitable selection of the values ​​of the set of cutting parameters. A user of the laser cutting machine 10 transmits a material of the workpiece 24 to the control unit 28. This can be done, for example, by displaying a selection of different materials using the display 30 and selecting one of the displayed materials by the user. The control unit 28 excludes those types of machining defects that do not occur with the transmitted material. In the example shown, the transmitted material is brass and the excluded machining defect is spontaneous combustion.

[0078] In an alternative embodiment not shown, the cutting edge was produced using a process gas in the form of nitrogen. The user of the laser cutting machine transmits the process gas in the form of nitrogen to the control unit. The control unit eliminates those types of processing errors that do not occur with the transmitted process gas.

[0079] The user creates digital image data of the machining error 14 with the camera 26 of the laser cutting machine 10 .

[0080] The control unit 28 analyzes the digital image data using a machine learning algorithm. The machine learning algorithm classifies the machining defect 14 as a burr based on the digital image data and the transmitted material.

[0081] In an alternative embodiment not shown, the machining defect and the type of machining defect can be a jet break, a melt tipping over, a slag adhesion, a wavy cut start, a groove trailing edge, a roughening, a pitting, a slagging, a welding of the cut edge, a cut surface discoloration, a cut edge discoloration, a corner discoloration, a corner discoloration or a cut end discoloration.

[0082] The control unit 28 has predefined optimization rules, with one of the predefined optimization rules being assigned to each classifiable type of machining error.

[0083] In the example shown, the classified type of machining defect 14 is the burr, and the set of cutting parameters includes the gas pressure 42. The predefined optimization rule associated with the burr includes increasing a value of the gas pressure 42 by +1.5 bar, +1.2 bar, +0.9 bar, +0.6 bar, and +0.3 bar.

[0084] The control unit 28 generates several modified sets of cutting parameters based on the classification by applying the predetermined optimization rule associated with the burr to the set of cutting parameters. This generates five modified sets of cutting parameters that differ from each other in a value of the gas pressure 42.

[0085] Using the modified sets of cutting parameters, additional cutting edges 16 are produced with the laser cutting machine, with each modified set of cutting parameters being used to produce at least one additional cutting edge 16. As a result, at least five additional cutting edges 16 are produced.

[0086] Fig. 4 shows the further cutting edges 16. For better understanding, the further cutting edges 16 are labeled with the numbers 1) to 5).

[0087] The further cutting edge 16 with the number 1) was manufactured with a gas pressure 42 that was 0.3 bar higher than that of the cutting edge 12. The further cutting edge 16 with the number 2) was manufactured with a gas pressure 42 that was 0.6 bar higher than that of the cutting edge 12. The further cutting edge 16 with the number 3) was manufactured with a gas pressure 42 that was 0.9 bar higher than that of the cutting edge 12. The further cutting edge 16 with the number 4) was manufactured with a gas pressure 42 that was 1.2 bar higher than that of the cutting edge 12. The further cutting edge 16 with the number 5) was manufactured with a gas pressure 42 that was 1.5 bar higher than that of the cutting edge 12.

[0088] By evaluating the occurrence of machining errors 22 for each of the additional cutting edges 16, an evaluation 18 of the additional cutting edges 16 is created. The evaluation is performed by a user.

[0089] On the display 30 in the form of a touchscreen monitor, a rating scale 20 is displayed for each additional cutting edge 16, see Fig. 4. The rating scale 20 has three stars. The user evaluates the occurrence, in particular the severity and frequency, of machining errors 22 for each of the additional cutting edges 16 and enters their rating into the rating scale 20.

[0090] The further cutting edge 16, numbered 1, has a severe machining error 22, which is why the user gives it a poor rating. The user selects no stars on the 20-star rating scale.

[0091] For the further cutting edge 16 (numbered 2), the machining error 22 is slightly pronounced, which is why the user assigns it a medium rating. The user selects one star on the rating scale 20.

[0092] The other cutting edge 16, numbered 3, is free of any machining errors, which is why the user gives it a good rating. The user selects three stars on the rating scale 20.

[0093] The other cutting edge 16, numbered 4, is free of any machining errors, which is why the user gives it a good rating. The user selects three stars on the rating scale 20.

[0094] For the further cutting edge 16 (numbered 5), the machining error 22 is slightly pronounced, which is why the user assigns it a medium rating. The user selects one star on the rating scale 20.

[0095] In an alternative embodiment not shown, the evaluation can be computer-implemented. For this purpose, the user creates digital image data of each additional cutting edge using the laser machine's camera, with the control unit evaluating the occurrence of machining errors for each of the additional cutting edges by analyzing the digital image data.

[0096] The control unit 28 determines a further cutting edge 16 that has the best rating in the evaluation 18 of the further cutting edges 16. These are the further cutting edges 16 with the numbers 3) and 4). The control unit 28 has determined a plurality of further cutting edges 16 and the control unit 28 creates the optimized set of cutting parameters by calculating the mean value of the modified sets of cutting parameters with which the determined further cutting edges 16 with the numbers 3) and 4) were produced. The mean value of the gas pressure 42 of the modified sets of cutting parameters of the further cutting edge 16 with the numbers 3) and 4) is a gas pressure 42 that is 1.05 bar higher than the set of cutting parameters.

[0097] The optimized set of cutting parameters differs from the set of cutting parameters in the value of the gas pressure 42. The value of the gas pressure 42 of the optimized set of cutting parameters is 1.05 bar higher than the value of the gas pressure 42 of the set of cutting parameters. Thus, the optimized set of cutting parameters is based on the modified set of cutting parameters with which the determined further cutting edge 16 was produced.

[0098] In an alternative embodiment not shown, for example, only the further cutting edge with the number 3) may be the best-rated further cutting edge. In this case, the optimized set of cutting parameters is equal to the modified set of cutting parameters with which the determined further cutting edge with the number 3) was produced. In this case, the gas pressure value of the optimized set of cutting parameters is 0.9 bar higher than the gas pressure value of the set of cutting parameters.

[0099] The control unit 28 replaces the set of cutting parameters with the optimized set of cutting parameters. Subsequent cut edges produced with the laser machine are produced using the set of cutting parameters, wherein the set of cutting parameters is equal to the optimized set of cutting parameters.

[0100] As the exemplary embodiments shown and explained make clear, the invention provides a method and a laser cutting machine which enables cost-effective production of cutting edges.

Claims

Patent claims 1. A method for replacing a set of cutting parameters for producing a cutting edge (12) with a laser cutting machine (10) with an optimized set of cutting parameters, the method comprising the steps of: a) producing the cutting edge (12) with the laser cutting machine (10) using the set of cutting parameters, wherein the cutting edge has a machining error (14), b) classifying the machining error (14) according to a type of machining error (14), c) generating a plurality of modified sets of cutting parameters based on the classification, d) producing further cutting edges (16) with the laser cutting machine (10) using the modified sets of cutting parameters, wherein each modified set of cutting parameters is used for producing a further cutting edge (16),e) Creating an evaluation (18) of the further cutting edges (16) by evaluating an occurrence of machining errors (22) for each of the further cutting edges (16), fl) Determining a further cutting edge (16) that is best evaluated in the evaluation (18) of the further cutting edges (16), g) Replacing the set of cutting parameters by the optimized set of cutting parameters, wherein the optimized set of cutting parameters is based on the, modified set of cutting parameters with which the determined further cutting edge was produced.

2. The method according to claim 1, wherein the classification of step b) is a computer-implemented classification.

3. Method according to one of the preceding claims, wherein the method after step a1) comprises the step of: a2) transmitting a material of a workpiece (24) having the cutting edge (12), wherein the classification of step b) is carried out based on the transmitted material.

4. Method according to one of the preceding claims, wherein the production of the cutting edge (12) of step a1) is carried out using a process gas, wherein the method after step a1) comprises the step: a6) transmitting the process gas used in producing the cutting edge (12), wherein the classification of step b) is carried out based on the transmitted process gas.

5. Method according to one of the preceding claims, wherein the method after step a1) comprises the step: a4) creating digital image data of the machining defect (14) with a camera (26) and / or a5) uploading digital image data of the machining defect (14), wherein the classification of step b) is carried out based on the digital image data.

6. Method according to one of the preceding claims, wherein the classification of step b) is carried out by means of an image recognition algorithm, an image comparison algorithm and / or a machine learning algorithm.

7. Method according to one of the preceding claims, wherein the evaluation of step e) is a computer-implemented evaluation or an evaluation by a user.

8. Method according to one of the preceding claims, wherein the type of machining defect (14) of the cut edge (12) is a burr, a jet break, a melt tipping over, a slag adhesion, a wavy cut start, a groove trailing edge, a roughening, a pitting, a spontaneous combustion, a slagging, a welding of the cut edge, a cut surface discoloration, a cut edge discoloration, a corner discoloration, a corner discoloration and / or a cut end discoloration.

9. Method according to one of the preceding claims, if in step f1) a single further cutting edge (16) is determined, the optimized set of cutting parameters is equal to the modified set of cutting parameters with which the determined further cutting edge was produced, or if in step f1) a plurality of further cutting edges (16) are determined, the method comprises the step after step f1): f2) creating the optimized set of cutting parameters by a Calculating the mean value of the modified sets of cutting parameters with which the several determined further cutting edges (16) were produced.

10. Method according to one of the preceding claims, wherein the generation of a plurality of modified sets of cutting parameters of step c) is carried out by applying a predetermined optimization rule to the set of cutting parameters.

11. The method according to any one of the preceding claims, wherein the set of cutting parameters comprises at least one cutting parameter from a set of focus diameter, laser power, nozzle focus distance, nozzle workpiece distance, feed rate, gas pressure, nozzle diameter and gas type, wherein generating a plurality of modified sets of cutting parameters of step c) comprises changing the at least one cutting parameter.

12. Laser cutting machine (10), wherein the laser cutting machine (10) is designed to carry out a method according to one of the preceding claims.

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