Method for replacing cutting parameter set for producing cutting edge by means of laser cutting machine with optimized cutting parameter set, and laser cutting machine

Abstract

The invention relates to a method for replacing a cutting parameter set for producing a cutting edge (12) by means of a laser cutting machine (10) with an optimized cutting parameter set, the method comprises the following steps: a1) generating a cutting edge (12) with a machining error (14) by means of a laser cutting machine (10) using a cutting parameter set, b) classifying the machining error (14) according to the type of machining error (14), c) generating a plurality of modified cutting parameter sets on the basis of the classification, d) producing further cutting edges (16) using the modified cutting parameter sets by means of the laser cutting machine (10), each modified cutting parameter set being used to produce a further cutting edge (16), e) creating an evaluation (18) of the further cutting edges (16) by evaluating the occurrence of a machining error (22) for each of the further cutting edges (16), f1) ascertaining a further cutting edge (16) that is evaluated to be optimal in the evaluation (18) of the further cutting edge (16), g) replacing the cutting parameter set with an optimized cutting parameter set, which is based on a modified cutting parameter set using which the ascertained further cutting edge is generated.

Description

Technical Field

[0001] This invention relates to a method for replacing a set of cutting parameters used to generate a cutting edge with an optimized set of cutting parameters, and to a laser cutting machine. Background Technology

[0002] Typically, a cutting parameter set is used to generate the cutting edge using a laser cutting machine. The cutting parameter set can also be called an adjustment parameter set, equipment adjustment parameter set, or scattering parameter set, especially for laser cutting machines used to generate the cutting edge. Generating the cutting edge can also be referred to as cutting the workpiece.

[0003] The cutting parameter set typically includes adjustment parameters for setting the laser machine, which in particular have a direct impact on the quality of the resulting cut edge.

[0004] The set of cutting parameters, and especially their values, typically depends on the characteristics of the workpiece. These characteristics can be, for example, the material and / or thickness of the workpiece. With proper selection of the cutting parameter set, and especially its values, a laser cutting machine can produce cutting edges with virtually no or no machining errors. With inappropriate selection of the cutting parameter set, and especially its values, machining errors such as burrs or rough textures on the cutting edges can occur, necessitating reprocessing of the resulting cutting edges or rendering the workpiece unusable.

[0005] DE 10 2019 127 323 A1 discloses a laser processing system for machining workpieces using a laser beam. The laser processing system includes a sensor unit for monitoring the machining process. The laser processing system also includes a computing unit that outputs adjustment data to a control unit of the laser processing system to optimize the machining process in each state through corresponding control actions and maintain the machining process in the optimized state.

[0006] Purpose of the invention The objective of this invention is to provide a method for generating cutting edges using an alternative set of cutting parameters at a cost-effective rate. Furthermore, the objective of this invention is to provide a laser cutting machine configured to perform this method. Summary of the Invention

[0007] This invention solves this task by providing a method having the features of claim 1 and a laser cutting machine having the features of claim 11. Advantageous extensions and / or configurations of the invention are described in the dependent claims.

[0008] The method according to the invention is configured to replace a set of cutting parameters used to generate a cutting edge by means of a laser cutting machine with an optimized set of cutting parameters. The method comprises the following steps: a1) generating a cutting edge using the set of cutting parameters by means of a laser cutting machine, wherein the cutting edge has processing errors; b) classifying the processing errors according to their type; c) generating multiple, particularly 5 or 10, modified sets of cutting parameters based on the classification; d) generating additional cutting edges using the modified sets of cutting parameters by means of a laser cutting machine, wherein each modified set of cutting parameters is used to generate an additional cutting edge; e) creating an evaluation of the additional cutting edges by assessing the occurrence of processing errors in each of the additional cutting edges; f1) determining the additional cutting edge that is evaluated as optimal in the evaluation of the additional 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 used to generate the determined additional cutting edges, particularly those determined in step f1).

[0009] Advantageously, this method is executed when needed, particularly when machining errors occur, which is why it does not generate a large amount of data when producing the cutting edge, and why it does not require powerful computational resources for generating the cutting edge. This makes it possible to generate the cutting edge with the help of cost-effective computational resources. Therefore, this method achieves cost-effective generation of cutting edges.

[0010] A cutting parameter set can have multiple, specifically one to ten cutting parameters. An optimized cutting parameter set can have multiple, specifically one to ten cutting parameters. A modified cutting parameter set can have multiple, specifically one to ten cutting parameters. The number of cutting parameters in a cutting parameter set, an optimized cutting parameter set, and a modified cutting parameter set can be the same.

[0011] The cutting parameter set can be a subset of the laser cutting machine's adjustment parameter set.

[0012] Cutting edges generated using an optimized set of cutting parameters can have no machining errors or have reduced machining errors compared to cutting edges generated using the same set of cutting parameters.

[0013] The optimized cutting parameter set can differ from the original cutting parameter set, at least in the values ​​of the cutting parameters. For example, the values ​​of the cutting parameters in the optimized cutting parameter set can be less than or greater than the values ​​of the cutting parameters in the original cutting parameter set.

[0014] Processing errors can be categorized by the user.

[0015] Classification can be described as categorizing, classifying, assigning, or determining. The type of machining error can be referred to as a category of machining error. Multiple categorizable machining error types can be predefined, where classifying a machining error involves selecting one type from these predefined categories.

[0016] If the cutting edge has multiple different types of machining errors, the machining errors can be classified according to the main type of machining error.

[0017] Generating a modified set of cutting parameters can include changing the set of cutting parameters. In other words, any modified set of cutting parameters can be obtained, specifically, acquired, by changing the set of cutting parameters. This change can be made based on the category of machining error, and in particular, the type of machining error being classified. The category of machining error, and in particular the type of machining error being classified, can define at least one cutting parameter of the set of cutting parameters to be changed. This change can be referred to as a variation.

[0018] Multiple modified cutting parameter sets can differ from each other, particularly in the number and / or value of the cutting parameters.

[0019] The assessment may include evaluating the occurrence of machining errors at each of the additional cutting edges. The evaluation of the occurrence of machining errors may be an assessment of their severity and / or frequency.

[0020] For example, another cut edge may receive a poor rating due to significant and / or frequent machining errors. Conversely, another cut edge may receive a good rating due to slightly significant and / or minimal machining errors.

[0021] The alternative cutting edge evaluated as optimal may be free of machining errors. Alternatively, the alternative cutting edge evaluated as optimal may be the one with the least noticeable and / or least machining errors.

[0022] In an extended version of this method, the classification in step b) is a computer-implemented classification. The classification can be performed by a control device, particularly a laser cutting machine. The control device can have (in particular) a computing device, a control unit, a microcontroller, and / or a computer. The classification can be performed locally, particularly by the laser cutting machine or a local server, or with the aid of cloud computing.

[0023] In an extended version of the method, after step a1), the method has the following steps: a2) conveying material of a workpiece with a cut edge. The classification in step b) is performed based on the conveyed material.

[0024] The method may include the following steps: a0) specifying the number of types of processing errors, and a3) limiting the number of types of processing errors based on the material being transported. The limited number of types of processing errors may include those types that occur when a cutting edge is produced in the transported material using a laser cutter. The limited number of types of processing errors may also exclude those types that do not occur when a cutting edge is produced in the transported material using a laser cutter.

[0025] The classification in step b) based on the material being transferred may mean that only a limited number of types of processing errors are available for classification. In other words, the types of processing errors classified in step b) may specifically be only one type of processing error from a limited number of types of processing errors.

[0026] The workpiece can be made of structural steel, stainless steel, aluminum, copper, or brass.

[0027] If the classification of processing errors is computer-implemented, then the material transfer in step a2) can be a transfer to a control device. If the processing errors are classified by a user, then the material transfer in step a2) can be a transfer to the user. If the processing errors are classified by a user, then alternatively or additionally, the material transfer in step a2) can be a transfer to a display of a type for displaying a limited number of processing errors in step a3), wherein the display is configured to perform step a3).

[0028] In an extended version of this method, the cutting edge in step a1) is generated using a process gas. Following step a1), the method includes the following step: a6) conveying the process gas used in generating the cutting edge. The classification in step b) is based on the conveyed process gas.

[0029] The method may include the following steps: a0) specifying the number of types of processing errors, and a7) limiting the number of types of processing errors based on the conveyed process gas. The limited number of types of processing errors may include types of processing errors that occur when the laser cutting machine uses the conveyed process gas to generate the cut edge. The limited number of types of processing errors may not include types of processing errors that do not occur when the laser cutting machine uses the conveyed process gas to generate the cut edge.

[0030] The classification of step b) based on the transmitted process gas can mean specifically that only a limited number of processing error types are available for classification. In other words, the type of processing error classified in step b) can specifically be only one type of processing error from a limited number of processing error types.

[0031] The process gas can be nitrogen or oxygen.

[0032] If the classification of processing errors is implemented by a computer, then the transmission of process gas in step a6) can be a transmission to a control device. If the classification of processing errors is performed by a user, then the transmission of process gas in step a6) can be a transmission to the user. If the classification of processing errors is performed by a user, then alternatively or additionally, the transmission of process gas in step a6) can be a transmission to a display for displaying the types of a limited number of processing errors in step a7), wherein the display is configured to perform step a7).

[0033] In an extended version of this method, following step a1), the method includes the following steps: a4) creating digital image data of the processing error using a camera, particularly a digital camera, and / or a5) uploading the digital image data of the processing error. The classification in step b) is performed based on the digital image data.

[0034] In an extended version of this method, the classification in step b) is performed using an image recognition algorithm, an image comparison algorithm, and / or a machine learning algorithm. The image recognition algorithm may have (in particular) feature extraction and / or feature simplification. The machine learning algorithm may have (in particular) a trained neural network. Specifically, the image recognition algorithm, image comparison algorithm, and / or machine learning algorithm can classify processing errors by analyzing digital image data.

[0035] In an extended version of this method, the evaluation in step e) is either a computer-implemented evaluation or an evaluation performed by a user.

[0036] If the assessment is performed by the user, step e) of creating an assessment for the additional cut edges may include displaying an assessment table for each additional cut edge and detecting the assessment using the assessment table for each additional cut edge. The assessment may include the user entering the assessment into the assessment table for each additional cut edge.

[0037] If the evaluation is computer-implemented, then step e) of creating an evaluation of the additional cut edges may involve the following steps: creating digital image data of each additional cut edge using a camera, and creating a value for the occurrence of processing errors in each of the additional cut edges by means of analysis of the digital image data, particularly computer-implemented analysis. The analysis of the digital image data may include applying image recognition algorithms, image comparison algorithms, and / or machine learning algorithms, particularly applying image recognition algorithms, image comparison algorithms, and / or machine learning algorithms to the digital image data. Image recognition algorithms may have (in particular) feature extraction and / or feature simplification. Machine learning algorithms may have (in particular) trained neural networks.

[0038] In an extended version of this method, the types of processing errors at the cutting edge include burrs, beam interruption, molten material spillage, slag adhesion, wavy cut initiation, groove tailing, rough texture, pitting, spontaneous combustion, slag formation, welding of the cutting edge, discoloration of the cutting surface, discoloration of the cutting edge, discoloration of the corner, and / or discoloration of the cutting end.

[0039] In an extended version of the method, if a single additional cut edge is obtained in step f1), the optimized cut parameter set is equal to the modified cut parameter set used to generate the obtained additional cut edge. Alternatively, if multiple additional cut edges are obtained in step f1), the method then proceeds after step f1) to the step of f2) creating the optimized cut parameter set by calculating the average of the modified cut parameter sets used to generate the multiple additional cut edges.

[0040] In an extended version of this method, step c) generates multiple modified cutting parameter sets by applying predefined optimization rules to the cutting parameter sets.

[0041] The predefined optimization rules can be rules on how to change the set of cutting parameters, especially their values ​​and / or their cutting parameters, to generate multiple modified sets of cutting parameters.

[0042] Predefined optimization rules can be assigned to the types of processing errors classified in step b).

[0043] For example, the type of machining error classified in step b) could be beam interruption, spontaneous combustion, or rough texture, and the cutting parameter set could have a feed, wherein the predefined optimization rules associated with beam interruption, spontaneous combustion, or rough texture could include reducing the value of the feed, for example, by 4%, 6%, 8%, 10%, 12%, or 14%, so that multiple, in particular six, modified cutting parameter sets are generated by reducing the value of the feed in the cutting parameter set.

[0044] For example, the type of processing error classified in step b) could be molten material spillage or slag adhesion, and the cutting parameter set could have a focal position. The predefined optimization rules associated with molten material spillage or slag adhesion could include changes to the value of the focal position, such as changing +2 mm, +1.5 mm, +1 mm, +0.5 mm, -0.5 mm, -1 mm, -1.5 mm, or -2 mm, so that multiple, particularly eight, modified cutting parameter sets are generated by changing the value of the focal position of the cutting parameter set. The abbreviation mm can refer to the unit millimeter.

[0045] For example, the type of machining error classified in step b) could be a wavy cut, and the cutting parameter set could have a nozzle-workpiece distance, wherein the predefined optimization rules associated with the wavy cut could include changes to the value of the nozzle-workpiece distance, such as changing +0.5 mm, +0.3 mm, +0.15 mm, -0.15 mm, -0.3 mm, or -0.5 mm, so that multiple, particularly six, modified cutting parameter sets are generated by changing the value of the nozzle-workpiece distance of the cutting parameter set.

[0046] For example, the type of processing error classified in step b) could be discoloration of the cut surface, and the cutting parameter set could have a gas pressure, wherein the predefined optimization rules associated with the discoloration of the cut surface could include increasing the value of the gas pressure, for example, increasing by +5 bar, +4 bar, +3 bar, +2 bar and +1 bar, such that multiple, in particular five, modified cutting parameter sets are generated by increasing the value of the gas pressure in the cutting parameter set.

[0047] For example, the type of processing error classified in step b) could be pitting, and the cutting parameter set could have a gas pressure, wherein the predefined optimization rules associated with pitting could include changes in the value of the gas pressure, such as changing +0.5 bar, +0.2 bar, +0.1 bar, -0.1 bar, -0.2 bar, or -0.5 bar, such that multiple, particularly six, modified cutting parameter sets are generated by changing the value of the gas pressure in the cutting parameter set.

[0048] Predefined optimization rules can be (in particular) heuristic, empirical rule-based, and / or machine learning methods.

[0049] Predefined optimization rules can change the value of one cutting parameter in the cutting parameter set, and subsequently change the value of another cutting parameter in the cutting parameter set.

[0050] Multiple modified cutting parameter sets can be generated in a waterfall or interactive manner.

[0051] The number of optimization rules can be predefined, wherein each optimization rule is assigned to a type, particularly a single processing error, which is preferably classified in step b). The number of optimization rules and the number of types of processing errors, particularly the number of types of processing errors that can be classified in step b), can be the same.

[0052] Step c) generating multiple sets of modified cutting parameters may include predefined optimization rules that are associated with the types of processing errors classified in step b).

[0053] In an extended version of this method, the cutting parameter set has at least one cutting parameter from the set of: focal diameter, laser power, nozzle-focal distance, nozzle-workpiece distance, feed, gas pressure, nozzle diameter, and gas type. Step c) of generating multiple modified cutting parameter sets includes changing at least one cutting parameter, specifically changing the value of at least one cutting parameter.

[0054] The laser cutting machine according to the present invention is configured to perform the above-described method. The laser cutting machine may have a control device for performing the above-described method, particularly a control device for classifying processing errors according to their type. The control device may include (in particular) a computing device, a microcontroller, a computer, a production guidance system, a manufacturing execution system, a memory programmable controller, and / or an IPC-based controller. Attached Figure Description

[0055] Further advantages and advantageous configurations of the invention can be derived from the following drawings, description, and claims. All features disclosed in the drawings, description, and claims, whether individually or in any desired combination, may be essential to the invention.

[0056] It shows: Figure 1 A schematic flowchart is shown for a method of replacing the set of cutting parameters used to generate the cutting edge by means of a laser cutting machine with an optimized set of cutting parameters. Figure 2 A schematic diagram of a laser cutting machine is shown, which causes the execution of a method for replacing the set of cutting parameters that produce the cut edge with an optimized set of cutting parameters. Figure 3 It shows the use of Figure 2 A schematic diagram of the cutting edge produced by a laser cutting machine, and Figure 4 It shows the use of Figure 2 A schematic diagram of the additional cutting edges produced by the laser cutting machine and a schematic diagram of the evaluation of the additional cutting edges. Detailed Implementation

[0057] Figure 1 A flowchart is shown for a method of replacing the set of cutting parameters used to generate the cut edge by a laser cutting machine with an optimized set of cutting parameters. The optimized set of cutting parameters differs from the original set of cutting parameters, at least in the values ​​of the cutting parameters.

[0058] The method comprises the following steps: a0) Specifying the number of types of machining errors. The predefined types of machining errors may be burrs, beam interruption, molten material spillage, slag adhesion, wavy cut beginning, groove tailing, rough texture, pitting, spontaneous combustion, slag formation, welding of cut edges, discoloration of cut surfaces, discoloration of cut edges, discoloration of corners, and / or discoloration of cut ends.

[0059] The method comprises the following steps: a1) using a laser cutting machine and a set of cutting parameters to generate a cutting edge, wherein the cutting edge has processing errors. The set of cutting parameters comprises multiple settings of the laser cutting machine for generating the cutting edge.

[0060] The method comprises the following steps: a2) conveying the material of a workpiece having a cutting edge. The laser cutting machine has a sensor for detecting the material. The sensor detects and conveys the material of the workpiece. Alternatively, the user of the laser cutting machine may convey the material of the workpiece.

[0061] In an alternative embodiment (not shown), the cutting edge in step a1) is generated using a process gas, and after step a1), the method has the following step: a6) delivering the process gas used in generating the cutting edge.

[0062] The method comprises the following steps: a3) limiting the number of processing error types based on the material being transported. The limited number of processing error types includes the types of processing errors that occur when a laser cutting machine generates a cutting edge into the transported material.

[0063] The method has the following steps: a4) Create digital image data of the processing error using a digital camera.

[0064] The method has the following steps: b) classifying the processing errors by computer based on the digital image data of the processing errors and the material being transported, according to the type of processing error.

[0065] In an alternative embodiment (not shown), if the process gas used in generating the cut edge is delivered, the classification in step b) is performed based on the delivered process gas.

[0066] The types of processing errors that are classified are not those that are excluded based on the material being transferred.

[0067] The classification is performed by the laser cutting machine's computing unit. This classification is accomplished using a machine learning algorithm. The machine learning algorithm is a trained neural network that classifies the types of processing errors.

[0068] The method comprises the following steps: c) generating multiple, particularly 5 or 10, modified cutting parameter sets based on the classification. Each modified cutting parameter set is obtained by changing the values ​​of the cutting parameter sets.

[0069] Predefined optimization rules are assigned to the types of machining errors being classified. Step c) generates multiple modified cutting parameter sets by applying predefined optimization rules to these sets. The predefined optimization rules define how to change the values ​​of the cutting parameter sets to generate multiple modified cutting parameter sets.

[0070] Multiple modified sets of cutting parameters differ from each other in at least one value of the cutting parameter.

[0071] The method comprises the following steps: d) using a laser cutter to generate additional cut edges using a modified set of cut parameters, wherein each modified set of cut parameters is used to generate additional cut edges.

[0072] The method comprises the following steps: e) creating an evaluation of the additional cutting edges by assessing the occurrence of machining errors at each of the other cutting edges. The assessment of the occurrence of machining errors is an evaluation of the significance and / or frequency of machining errors at each of the other cutting edges. The additional cutting edge evaluated as optimal may have no machining errors. Alternatively, the additional cutting edge evaluated as optimal may be the additional cutting edge with the least significant and / or least occurring machining errors.

[0073] The evaluation is computer-implemented. Creating an evaluation of the additional cutting edges involves creating digital image data of each additional cutting edge using a camera, and then using computer-implemented analysis of the digital image data to assess the occurrence of processing errors at each of the additional cutting edges. The computer-implemented analysis of the digital image data involves applying machine learning algorithms to the digital image data. The machine learning algorithm is a trained neural network used to evaluate the occurrence of processing errors.

[0074] The method has the following steps: f1) finding the alternative cutting edge that is evaluated as the best in the evaluation of alternative cutting edges.

[0075] If a single additional cutting edge is obtained in step f1), the optimized cutting parameter set is equal to the modified cutting parameter set used to generate the obtained additional cutting edge.

[0076] If multiple additional cutting edges are obtained in step f1), then after step f1), the method has the following step: f2) creating an optimized cutting parameter set by calculating the average of the modified cutting parameter set used to generate the multiple additional cutting edges. Specifically, the values ​​of the cutting parameters in the optimized cutting parameter set are equal to the average values ​​of the cutting parameters in the modified cutting parameter set used to generate the multiple additional cutting edges.

[0077] The method has the following steps: g) replacing the cutting parameter set with an optimized cutting parameter set, wherein the optimized cutting parameter set is a modified cutting parameter set based on the additional cutting edges obtained in step f1) generated therefrom.

[0078] Figure 2 A laser cutting machine 10 is shown, which causes the execution of a method for replacing the set of cutting parameters that produce the cutting edge with an optimized set of cutting parameters.

[0079] The laser cutting machine 10 includes 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 configured to operate the laser cutting head 32 in order to generate a cutting edge 12 using the laser cutting machine 10.

[0080] The control unit 28 has a set of cutting parameters for generating the cutting edge. The cutting parameter set includes the following cutting parameters: focal diameter, laser power, nozzle-focal distance 36, nozzle-workpiece distance 38, feed 40, gas pressure 42, nozzle diameter, and gas type.

[0081] Using a set of cutting parameters and other settings, a laser cutting machine 10 generates a cutting edge 12. The generation of the cutting edge 12 involves cutting the workpiece 24. The workpiece 24 includes the cutting edge 12.

[0082] Figure 3 A workpiece 24 with a cut edge 12 is shown, which is generated using a laser machine 10 using a set of cutting parameters. The cut edge 12 has a machining error 14 in the form of burrs. The machining error 14 occurs due to an inappropriate selection of the values ​​of the cutting parameter set.

[0083] The user of the laser cutting machine 10 transfers the material of the workpiece 24 to the control unit 28. This can be accomplished, for example, by means of a display 30 showing the selection of different materials and the user selecting one of the displayed materials. The control unit 28 excludes types of processing errors that would not occur in the transferred material. In the illustrated embodiment, the transferred material is brass, and the excluded processing error is spontaneous combustion.

[0084] In an alternative implementation (not shown), the cutting edge is generated using a process gas in the form of nitrogen. The user of the laser cutting machine supplies the process gas in the form of nitrogen to the control unit. The control unit eliminates types of processing errors that would not occur with the supplied process gas.

[0085] The user uses the camera 26 of the laser cutting machine 10 to create digital image data of the processing error 14.

[0086] The control unit 28 analyzes digital image data using a machine learning algorithm. The machine learning algorithm classifies the processing error 14 as burrs based on the digital image data and the material being transported.

[0087] In alternative embodiments (not shown), the processing error and the type of processing error can be beam interruption, molten material spillage, slag adhesion, wavy cut beginning, groove tailing, rough texture, pitting, slag formation, welding of the cut edge, discoloration of the cut surface, discoloration of the cut edge, discoloration of the corner, discoloration of the corner or discoloration of the cut end.

[0088] The control unit 28 has predefined optimization rules, one of which is assigned to each type of categorizable processing error.

[0089] In the illustrated embodiment, the type of the classified processing error 14 is burr, and the cutting parameter set has a gas pressure 42. Predefined optimization rules associated with burrs include increasing the value of the gas pressure 42 by +1.5 bar, +1.2 bar, +0.9 bar, +0.6 bar, and +0.3 bar.

[0090] The control unit 28 generates multiple modified cutting parameter sets based on classification by applying predefined optimization rules associated with burrs to the cutting parameter sets. This creates five modified cutting parameter sets that differ from each other in terms of the value of gas pressure 42.

[0091] Using modified sets of cutting parameters, an additional cutting edge 16 is generated using a laser cutting machine, wherein each modified set of cutting parameters is used to produce at least one additional cutting edge 16. This produces at least five additional cutting edges 16.

[0092] Figure 4 Another cut edge 16 is shown. For better understanding, the other cut edges 16 are labeled with numbers 1) to 5).

[0093] The additional cutting edge 16 marked with number 1) is generated at a gas pressure 42 that is 0.3 bar higher than the gas pressure of cutting edge 12. The additional cutting edge 16 marked with number 2) is generated at a gas pressure 42 that is 0.6 bar higher than the gas pressure of cutting edge 12. The additional cutting edge 16 marked with number 3) is generated at a gas pressure 42 that is 0.9 bar higher than the gas pressure of cutting edge 12. The additional cutting edge 16 marked with number 4) is generated at a gas pressure 42 that is 1.2 bar higher than the gas pressure of cutting edge 12. The additional cutting edge 16 marked with number 5) is generated at a gas pressure 42 that is 1.5 bar higher than the gas pressure of cutting edge 12.

[0094] An evaluation 18 for the other cut edges 16 is created by assessing the occurrence of processing errors 22 in each of the other cut edges 16. The evaluation is performed by the user.

[0095] On a display 30 in the form of a touchscreen monitor, an evaluation table 20 is displayed for each additional cut edge 16, see [link / reference]. Figure 4 The rating scale 20 has three stars. The user evaluates the occurrence, especially the severity and frequency, of the processing error 22 for each of the other cut edges 16 and enters the rating into the rating scale 20.

[0096] The additional cut edge 16, marked with number 1), has a very noticeable processing error 22, which is why it received a negative rating from the user. The user did not select any stars on rating scale 20.

[0097] In the other cut edge 16 marked 2), the machining error 22 is weakly displayed, which is why it received a moderate rating from the user. The user selects one star on rating scale 20.

[0098] The additional cut edge 16, marked with number 3, had no machining errors, which is why it received a good rating from the user. The user selected three stars on rating scale 20.

[0099] The additional cut edge 16, marked with number 4, had no machining errors, which is why it received a good rating from the user. The user selected three stars on rating table 20.

[0100] In the other cut edge 16 marked with number 5), the machining error 22 is weakly displayed, which is why it received a moderate rating from the user. The user selects one star on rating scale 20.

[0101] In an alternative implementation (not shown), the evaluation can be computer-based. For this purpose, the user creates digital image data of each additional cutting edge using the laser machine's camera, wherein the control unit evaluates the occurrence of processing errors at each of the additional cutting edges by analyzing the digital image data.

[0102] Control unit 28 selects the additional cutting edges 16 that are evaluated as optimal in the evaluation 18 of the additional cutting edges 16. These are the additional cutting edges 16 numbered 3) and 4). Control unit 28 has selected multiple additional cutting edges 16, and control unit 28 creates an optimized cutting parameter set by calculating the average value of the modified cutting parameter set used to generate the selected additional cutting edges 16 numbered 3) and 4). The average gas pressure 42 of the modified cutting parameter set of the additional cutting edges 16 numbered 3) and 4) is a gas pressure 42 increased by 1.05 bar compared to the cutting parameter set.

[0103] The optimized cutting parameter set differs from the original cutting parameter set in the value of gas pressure 42. The optimized cutting parameter set has a gas pressure 42 value that is 1.05 bar higher than that of the original cutting parameter set. Therefore, the optimized cutting parameter set is based on a modified cutting parameter set used to generate the additional cutting edge 16 obtained.

[0104] For example, in an alternative embodiment (not shown), only the additional cutting edge with number 3) can be the additional cutting edge evaluated as optimal. In this case, the optimized cutting parameter set is equal to the modified cutting parameter set used to generate the desired additional cutting edge with number 3). In this case, the gas pressure value of the optimized cutting parameter set is 0.9 bar higher than the gas pressure value of the cutting parameter set.

[0105] Control unit 28 replaces the cutting parameter set with an optimized cutting parameter set. The subsequent cutting edge generated by the laser machine is generated using the cutting parameter set, which is equal to the optimized cutting parameter set.

[0106] As clearly shown and illustrated, the present invention provides a method and laser cutting machine that enable cost-effective production of cutting edges.

Claims

1. A method for replacing a set of cutting parameters for generating a cutting edge (12) using a laser cutting machine (10) with an optimized set of cutting parameters, wherein, The method comprises the following steps: a1) The laser cutting machine (10) uses the cutting parameter set to generate the cutting edge (12), wherein the cutting edge has a processing error (14). b) Classify the machining error (14) according to its type. c) Generate multiple modified cutting parameter sets based on the classification. d) Using the laser cutter (10), additional cutting edges (16) are generated using the modified cutting parameter set, wherein each modified cutting parameter set is used to generate additional cutting edges (16). e) An evaluation (18) of the additional cutting edges (16) is created by assessing the occurrence of processing errors (22) in each of the additional cutting edges (16). f1) Find the other cutting edge (16) that is evaluated as the best in the evaluation (18) of the other cutting edge (16). g) Replace the cutting parameter set with the optimized cutting parameter set, wherein the optimized cutting parameter set is based on the modified cutting parameter set, and the modified cutting parameter set is used to generate the additional cutting edge to be obtained.

2. The method according to claim 1, - in, The classification described in step b) is a computer-implemented classification.

3. The method according to any one of the preceding claims, - Wherein, after step a1), the method has the following steps: a2) conveying material of the workpiece (24) having the cut edge (12), - in, The classification in step b) is performed based on the material being transported.

4. The method according to any one of the preceding claims, - in, The cutting edge (12) in step a1) is generated using process gas. - Wherein, after step a1), the method has the following step: a6) conveying the process gas used in generating the cutting edge (12), - Wherein, the classification in step b) is based on the process gas being transported.

5. The method according to any one of the preceding claims, - Among them, Following step a1), the method comprises the following steps: a4) creating digital image data of the processing error (14) using a camera (26) and / or a5) uploading the digital image data of the processing error (14). - Wherein, the classification in step b) is based on the digital image data.

6. The method according to any one of the preceding claims, - in, The classification in step b) is performed using image recognition algorithms, image comparison algorithms, and / or machine learning algorithms.

7. The method according to any one of the preceding claims, - in, The assessment described in step e) is either a computer-implemented assessment or an assessment performed by a user.

8. The method according to any one of the preceding claims, - Wherein, the processing error (14) of the cutting edge (12) is of the type of burr, beam interruption, melt pouring, slag adhesion, wavy cut, groove tail, rough texture, pitting, spontaneous combustion, slag formation, welding of the cutting edge, discoloration of the cutting surface, discoloration of the cutting edge, discoloration of the corner, discoloration of the corner and / or discoloration of the cutting end.

9. The method according to any one of the preceding claims, - If a single additional cutting edge (16) is obtained in step f1), then the optimized cutting parameter set is equal to the modified cutting parameter set described below, which is used to generate the obtained additional cutting edge, or - If multiple additional cutting edges (16) are obtained in step f1), the method then has the following steps after step f1: f2) to create the optimized cutting parameter set by calculating the average of the modified cutting parameter set used to generate the multiple additional cutting edges (16) obtained.

10. The method according to any one of the preceding claims, - in, Step c) generates multiple modified cutting parameter sets by applying predefined optimization rules to the cutting parameter sets.

11. The method according to any one of the preceding claims, - in, The cutting parameter set has at least one cutting parameter from the following set: focal diameter, laser power, nozzle-focal distance, nozzle-workpiece distance, feed, gas pressure, nozzle diameter, and gas type. - Wherein, step c) of generating multiple modified cutting parameter sets includes changing at least one cutting parameter.

12. A laser cutting machine (10), - wherein the laser cutting machine (10) is configured to perform the method according to any one of the preceding claims.

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