Optimization method for a laser processing process, optimization system and laser processing system

Abstract

The invention relates to an optimization method (10) for adapting processing parameters (12) in a laser processing method for processing at least one workpiece (16; 16a-h) with a laser processing machine (14), comprising the method steps: f) providing (18) at least one workpiece contour (20; 20a-k) of the workpiece (16; 16a-h) to be manufactured; g) segmenting (26) the workpiece contour (20; 20a-k) by dividing the at least one workpiece contour (20; 20a-k) into contour segments (28; 28a-h); h) determining (30) at least one monitoring segment (32; 32a-e) in the workpiece contour (20; 20a-k) from the contour segments (28; 28a-h); i) determining (40) predetermined optimal processing parameters (38; 38a-d) for the monitoring segment (32; 32a-e) by comparing the monitoring segment (32; 32a-e) with stored reference segments (36; 36a-d); j) Outputting (42) the determined optimal machining parameters (38; 38a-d). The invention also relates to an optimization system (46) and a laser processing system (44).

Description

BACKGROUND OF THE INVENTIONThe invention relates to an optimization method for a laser processing method. The invention also relates to an optimization system and a laser processing system with a laser processing machine.In the workpiece processing by means of laser processing machines, a processing order, typically the processing of a panel blank with a plurality of workpieces to be manufactured, is carried out by means of a predetermined processing parameter set. The machining parameter set usually comprises a combination of different individual machining parameters which have been determined to be particularly suitable for the machining. The machining parameter set must be selected by an operator, depending on a material to be machined, for example steel, aluminum, etc., and / or a workpiece geometry, for example a workpiece thickness, a workpiece contour, etc., from a plurality of stored machining parameter sets, which are provided by means of test series in advance of the machining in a complicated and cost-intensive manner. If an unsuitable machining parameter set is selected by the operator, this leads to a reduced machining quality, to incorrect machining and / or to a decreasing productivity.Moreover, even an optimally selected set of processing parameters represents a compromise in the processing of the panel blank. Typically, a panel blank comprises, for example, different workpiece geometries and material inhomogeneities, so that the machining with the predetermined machining parameters is optimum only for a part of the panel blank to be machined. The determination of a plurality of machining parameter sets for specific partial regions or workpieces of the panel blank increases the preparation effort by the operator considerably. Moreover, deviations in the material homogeneity can be difficult or not recognized at all by the operator, whereby even the selection of different machining parameter sets cannot ensure optimum machining.DE 10 2017 105 224 A1 discloses a machine learning device, a laser processing system and a machine learning method. To optimize the manufacturing process, state variables during manufacturing, in particular reflection light, are observed and taken into account when adapting the machining parameters. The previously known device performs an optimization without taking into account the workpiece geometry and material homogeneity, so that the aforementioned problems continue to exist.Object of the InventionIt is an object of the invention to improve the machining quality of workpieces by means of a laser machining machine, to avoid miscuts and to increase the productivity of the laser machining machine.DESCRIPTION OF THE INVENTIONThe underlying object is achieved according to the invention by an optimization method having the features of claim 1. In addition, the object is achieved by an optimization system having the features of claim 10.According to the invention, an optimization method is provided.The optimization method is designed for adapting processing parameters in a laser processing method. The laser processing method is typically configured for processing at least one workpiece with a laser processing machine.The optimization method has at least the following method steps:In a method step a) of the optimization method, provision is made for at least one workpiece contour of the workpiece to be produced. A workpiece contour is understood to mean a self-contained contour of the workpiece. A workpiece contour can be formed as an inner contour or an outer contour of the workpiece. The workpiece contour preferably has geometry information of the workpiece. Typically, the workpiece contour is provided in the form of machine-readable data and / or in the form of graphical data. As a result, the optimization method can be used flexibly.In a subsequent method step b), the workpiece contour is segmented by dividing the at least one workpiece contour into contour segments. In other words, the workpiece contour is divided into one or more sections. Typically, the workpiece contour is divided into a plurality of contour segments. The contour segments can have overlapping geometric sections of the workpiece contour. For example, a contour segment in the form of the entire workpiece contour and further contour segments can be formed as partial sections of the workpiece contour.In addition, according to the optimization method according to the invention, a method step c) is provided, in which at least one monitoring segment in the workpiece contour is ascertained from the contour segments. In other words, the contour segments are checked to see whether a monitoring segment is present.A monitoring segment is understood to mean a contour segment for which a machining error and / or machining with reduced machining quality is known. The machining error and / or the machining with reduced machining quality can be stored as a reference segment before the machining start and / or during the machining when a machining error or a machining with reduced machining quality is determined.A machining error may occur, for example, but not limited to, as a miscut, as a laser beam break, as a collision between a machining nozzle of the laser machining machine and the panel blank, and / or as thermal deformation of the panel blank. A machining error can be attributable, for example, but not exclusively, to too high a machining speed, too high a machining performance, too low a material quality, in particular local material quality, and / or too high a geometrical complexity of the workpiece.A further method step d) of the optimization method provides for determining predetermined optimum machining parameters for the monitoring segment by comparing the monitoring segment with stored reference segments. In other words, processing parameters are determined which have led to successful or high-quality processing in the case of similar contour segments.In addition, in a method step e) of the optimization method, the determined optimum machining parameters are output. The optimum processing parameters can be output to a human operator of the laser processing machine, preferably via a display of the laser processing machine. The optimum processing parameters are preferably output to a machine controller of the laser processing machine. This allows the processing parameters to be adapted particularly quickly and automatically.In summary, according to the invention, an optimization method is proposed in which a dynamic adaptation of the predetermined processing parameters of the laser processing machine can take place by means of a segment-based determination of optimum processing parameters. In this case, the machining process, in particular a laser cutting process, is continuously monitored with regard to possible problematic contour segments and machining errors taking into account the geometrical complexity, as a result of which a rapid adaptation of the machining parameters can take place. As a result, machining errors can be reduced and the machining quality can be kept high.In a preferred embodiment of the optimization method, in method step a) a plurality of workpiece contours of the workpiece to be produced are provided. Typically, at least all workpiece contours of a workpiece to be produced are provided. In this way, the at least one workpiece to be produced can be completely monitored.Alternatively or additionally, at least one workpiece contour of a plurality of workpieces to be produced is provided. This makes it possible to optimize the laser processing method more effectively.A development of the optimization method is preferred, in which in method step b) the segmenting of at least two workpiece contours of an individual workpiece takes place. Alternatively or additionally, it can be provided that segmenting of at least one workpiece contour of at least two different workpiece geometries takes place.Preferably, all workpiece contours of all workpieces to be produced of a production job are provided. A manufacturing order typically comprises the workpieces to be manufactured on a single panel blank with a predetermined machining parameter set. As a result, the optimization method can be used particularly effectively.An embodiment of the optimization method is preferred in which the segmenting of the workpiece contour takes place as a function of a geometric complexity, in particular a change in the contour curvature and / or a change in the contour profile. The geometrical complexity can be understood as a degree for a dynamic change of the machining parameters. Typically, a high degree of complexity requires a greater change in the predetermined processing parameters. Workpieces with high geometrical complexity therefore typically have frequent machining errors and / or machinings with reduced machining quality.An embodiment of the optimization method is further preferred, in which in method step c) the monitoring segment is determined by assigning an error position of a machining error of the laser machining machine. In other words, a contour segment can be determined as a monitoring segment by the occurrence of a machining error on the contour segment. As a result, the optimization method can be updated, in particular with respect to previously unknown monitoring segments, and further machining errors on similar contour segments can be avoided.In a preferred embodiment of the optimization method, in method step c) the monitoring segment is determined by comparing the contour segments with stored reference segments. In other words, problematic contour segments can be identified by comparison with already known problematic contour segments. As a result, a change in the machining parameters can already be made in advance of a machining error and a possible machining error can be prevented.An embodiment of the optimization method is particularly preferred, in which in method step b) each workpiece contour of each workpiece to be produced of a provided production order is segmented, wherein in method step c) monitoring segments are determined in all contour segments. As a result, the optimization method can be applied particularly effectively to the entire production order.In a preferred embodiment of the optimization method, preset machining parameters of the laser machining machine are replaced by the optimum machining parameters at least for machining at least one monitoring segment. This makes it possible to prevent the machining of a problematic contour segment or workpiece section with unsuitable machining parameters.In a particular embodiment of the optimization method, the predetermined machining parameters can first be provided and compared with the determined optimum machining parameters. Preferably, only the predetermined machining parameters are subsequently replaced, which differ from the optimum machining parameters by a predetermined value. This allows adaptation to be carried out taking into account the predetermined processing parameters.An embodiment of the optimization method is further preferred, in which method steps b), c) and d) are carried out by an algorithm, in particular a self-learning algorithm. A self-learning algorithm enables the particularly fast and effective segmenting of the workpiece contours, determining monitoring segments and determining optimum machining parameters.Typically, the algorithm, in particular a self-learning algorithm, is trained with a plurality of generated training data acquired by measurement technology before a machining of workpieces by the laser machining machine. Training data can be processing parameters, for example a laser power, a processing speed, a nozzle-workpiece distance, a focal position. Training data may further be panel blank data, for example a material quality of the panel blank, a material type of the panel blank, a material thickness of the panel blank. Training data can also be production information, for example a workpiece contour, a contour segment, a geometrical complexity, a machining error, etc. The training data is typically used in conjunction with the processing result generated by the corresponding training data for training the self-learning algorithm.The underlying object is also achieved by an optimization system.The optimization system is designed for optimizing the processing of workpieces by a laser processing machine. In addition, the optimization system is set up to carry out the optimization method described above and below.The optimization system comprises an evaluation unit. The evaluation unit is designed and configured for segmenting provided workpiece contours described above and below, for ascertaining monitoring segments in at least one workpiece contour and for determining optimum machining parameters.The optimization system also comprises an output unit for outputting the optimum machining parameters. The output unit can be designed for graphical and / or acoustic output of the optimum processing parameters. Alternatively or additionally, it can be provided that the output unit is designed for outputting the optimum machining parameters to the laser machining machine.In a particular embodiment of the optimization system, the evaluation unit has a self-learning algorithm described above and below for carrying out the method steps b), c) and d) of the optimization method described above and below.The underlying object is furthermore achieved by a laser processing system. The laser processing system has at least one laser processing machine for processing workpieces. According to the invention, the laser processing system also has an optimization system described above and below.Further advantages of the invention will become apparent from the description and the drawing. Likewise, the features mentioned above and those set out further below can be used according to the invention individually or together in any desired combinations. The embodiments shown and described are not to be understood as a final enumeration, but rather have exemplary character for describing the invention.DETAILED DESCRIPTION OF THE INVENTION AND DRAWINGFIG. 1 shows a schematic illustration of an optimization method for adapting processing parameters in a laser processing method. FIG. 2 shows schematically a laser processing system with an optimization system and a laser processing machine for manufacturing workpieces from a panel blank. FIG. 3 shows a schematic illustration of the panel blank from FIG. 2.FIG. 1 schematically shows an optimization method 10 according to the invention.The optimization method 10 is suitable and designed for adapting processing parameters 12 in a laser processing method, not shown in detail.Machining parameters 12 typically relate to machine parameters of a laser machining machine 14 (see FIG. 2 ), with which machining of one or more workpieces 16 (see FIGS. 2, 3 ) is carried out while maintaining a high machining quality and machining speed. Typical machine parameters of a laser processing machine 14 are, for example, a laser power, a feed speed and / or a focal position of a processing laser beam (not shown) of the laser processing machine 14.The optimization method 10 has at least the following method steps:In a method step 18 of the optimization method, at least one workpiece contour 20 (see FIGS. 2, 3 ) of the at least one workpiece 16 to be produced is provided.The provision of the at least one workpiece contour 20 is typically carried out by transmitting a production plan 22 (see FIG. 2 ) which comprises machine-readable geometry information of the workpieces 16 to be produced, in particular information relating to the workpiece contour 20.Alternatively or additionally, it can be provided that the workpiece contour 20 is provided by transmitting a graphical image. Typically, the workpiece contour 20 can be determined from a graphic representation of the workpiece 16 to be produced, in particular using graphic evaluation algorithms.Preferably, in method step 18, as shown in FIG. 2, a plurality of workpiece contours 20 are provided. The workpiece contours 20 can relate to a single workpiece 16 to be produced and / or to a plurality of workpieces 16 to be produced. Typically, when transmitting the production plan 22, the geometry information relating to all workpieces 16 to be produced of a panel blank 24 to be machined (see FIGS. 2, 3 ) is provided, such that preferably all workpiece contours 20 of all workpieces 16 to be produced are provided. As a result, the optimization method 10 can be used particularly effectively.A further method step 26 of the optimization method 10 provides for segmenting the at least one workpiece contour 20. The segmenting comprises dividing the provided at least one workpiece contour 20 into contour segments 28 (see FIGS. 2, 3 ). Preferably, at least two provided workpiece contours 20 of a single workpiece 16 to be manufactured or of two different workpieces 16 to be manufactured are segmented.The segmenting or dividing of the workpiece contour 20 into contour segments 28 typically comprises an evaluation of the geometry information of the workpiece contours 20.In particular, when evaluating the geometry information, a change in a contour curvature of the workpiece contour 20 can be taken into account. A change in the contour curvature typically causes a change in the machining parameters 12 of the laser machining machine 14 in order to keep machining accuracy high.In particular, when evaluating the geometry information, a change of a contour profile of the workpiece contour 20 can be taken into account. A change in the contour profile typically points to a strong change in direction of the workpiece contour 20, which is usually associated with a change in the machining parameters 12 of the laser machining machine 14.Preferably, a workpiece surface 29 enclosed by a workpiece contour 20 (see FIG. 2 ) can be taken into account when evaluating the geometry information. For example, a small workpiece surface 29 can cause a high energy input per unit area during the machining, which can lead to deformations of the panel blank 24.Further preferably, knowing the workpiece surface 29, a degree of geometric complexity of the workpiece 16 to be machined can be deduced. Particularly preferably, the workpiece surface 29 can be brought into a ratio to a contour length of the workpiece contour 20 delimiting the workpiece surface 29. Typically, the degree of geometric complexity increases with increasing contour length or decreasing workpiece surface 29.In particular, when evaluating the geometry information, a distance between two or more partial sections of the workpiece contour 20 and / or a distance between two or more workpiece contours 20 can be taken into account. A close distance can cause a thermal overloading or local deformation of the panel blank 24, so that the machining parameters, in particular the laser power, or the production sequence must be changed.Segmenting or dividing the workpiece contour 20 can comprise overlapping contour segments 28. In other words, a partial section of a workpiece contour 20 can be assigned to two or more contour segments 28. As a result, the optimization method 10 can be carried out even more precisely.A further method step 30 of the optimization method 10 provides for ascertaining at least one monitoring segment 32 (see FIGS. 2, 3 ) in the workpiece contour 20 from the contour segments 28.A monitoring segment 32 can be understood as a section of the workpiece contour 20 in which machining by the laser machining machine 12 with the set machining parameters 12 can lead to a reduced machining quality and / or to a machining error 34 (see FIG. 3 ).A machining error 34 or machining with reduced machining quality may be performed and detected during the machining of previous workpieces on a reference segment 36 (see FIG. 2 ). Typically, the machining parameters 12 in the preceding machining method have been corrected in sequence in order to achieve a more optimum machining result. By storing the reference segment 36, preferably in conjunction with optimum machining parameters 38 (see FIG. 2 ), a monitoring element 32 can be determined by comparing the contour segments 28 with the stored reference segments 36.Alternatively or additionally, the monitoring segment 32 can be ascertained by assigning the machining error 34 detected during the machining by the laser machining machine 12 or a detected machining with a reduced machining quality. For example, a miscut by the laser processing machine 14 can be detected by detecting elevated process radiation. Such a defect cut can be assigned, for example, to a locally deviating material condition of the panel blank 24, which requires a change of the machining parameters 12.An error message relating to this is output by the laser processing machine 14 in conjunction with an error position 39 (see FIG. 3 ) of the processing error 34 or the position of the processing with reduced processing quality. The contour segment 28 can be determined as a monitoring segment 32 and / or as a reference segment 36 by assigning it to the fault position 39. As a result, during the further and / or future machining, the renewed occurrence of the machining error 34 and / or the machining with reduced machining quality on similar sections of workpiece contours 20 can be prevented by the optimization method 10.Preferably, the optimization method 10 is designed such that all contour segments 28, in particular after the complete segmenting of all workpiece contours 20 of the workpieces 16 to be produced, are checked for possible monitoring segments 32. This allows a particularly comprehensive evaluation to be carried out and incorrect processing to be reliably prevented.In particular, the optimization method 10 is designed to perform a determination of monitoring segments 32 several times, in particular continuously, during the processing by the laser processing machine 12. In other words, the method step 30 can be carried out a plurality of times, in particular continuously.A further method step 40 of the optimization method 10 provides for determining predetermined optimum machining parameters 38 for the monitoring segment 32. In other words, processing parameters 38 are determined which enable processing of the monitoring segment 32 with high processing quality and / or manufacturing speed.The determination of optimum machining parameters 38 can be effected by comparing the monitoring segment 32 with stored reference segments 36. Typically, optimum machining parameters 38 are stored for the stored reference segments 36, which have led to optimum machining results in previous machinings. In other words, processing parameters 12 can be determined for which a defect-free processing of a workpiece 16 with a similar contour segment 28 or geometry segment is known.For determining the optimum machining parameters 38, it can be provided that the preset machining parameters 12 of the laser machining machine 14 are first provided. Typically, the preset machining parameters 12 can be evaluated from the manufacturing plan 22. Subsequently, it can be provided that the preset machining parameters 12 are compared with the optimum machining parameters 38 and subsequently replaced or adapted. Furthermore, it can be provided that the predetermined machining parameters 12 are approximated to the optimum machining parameters 38. This allows a compromise to be made between the predetermined machining parameters 12 and the optimum machining parameters 38.A further method step 42 of the optimization method 10 provides for outputting the determined optimum machining parameters 38. The output of the optimum machining parameters 38 can take place graphically.Alternatively or additionally, it can be provided that the optimum processing parameters 38 are output to the laser processing machine 14, in particular in the form of machine-readable data.Preferably, the output optimal machining parameters 38 on the laser machining machine 14 replace the preset machining parameters 12 at least for machining at least one monitoring segment 32.In a special embodiment of the optimization method 10, it can be provided that the method steps 26, 30, 40 are carried out by an in particular self-learning algorithm. As a result, a plurality of contour segments 20 of a plurality of workpieces 16 can be checked for monitoring segments 32 and an especially fast and accurate output of optimum machining parameters 38 can take place.FIG. 2 shows a schematic illustration of a laser processing system 44.The laser processing system 44 comprises a laser processing machine 14 for processing workpieces 16, here the workpieces 16 a- 16 g, by means of a processing laser beam (not shown).The laser processing system 44 also has an optimization system 46 which is configured to carry out the optimization method 10 described above and below (see FIG. 1 ).The optimization system 46 has an evaluation unit 48 for segmenting the provided workpiece contours 20. The evaluation unit 48 is also designed to ascertain monitoring segments 32. Furthermore, the evaluation unit 48 is configured to determine optimum machining parameters 38, here optimum machining parameters 38 a- d.The evaluation unit 48 preferably has an, in particular self-learning, algorithm for carrying out the method steps 26, 30, 40 (see FIG. 1 ).The optimization system 46 also has an output unit 50 for outputting the optimum machining parameters 38. According to the illustration, the output unit 50 is designed for communication or for data exchange with a machine controller 52 of the laser processing machine 14. As a result, the optimum processing parameters 38 can be transmitted particularly quickly and automatically.A sequence of the optimization method 10 can provide that, at the beginning of the production of the workpieces 16 a- h, as illustrated, a production plan 22 is transmitted by the machine controller 52 of the laser processing machine 14 to the optimization system 46. The production plan 22 typically contains at least one workpiece contour 20 of a workpiece 16 to be produced, here the workpiece contours 20 a- kof the workpieces 16 ato be produced from the panel blank 24. The workpieces 16 can be cut from the panel blank 24, for example, by means of a processing laser beam of a laser cutting machine.The workpieces 16 a- eeach have a workpiece contour 20 a- e. The workpieces 16 f- heach have an outer workpiece contour 20 f- hand each have an inner workpiece contour 20 i- k. In other words, the workpieces 16 f- hhave two workpiece contours 20. Moreover, it is also conceivable, however, for the workpieces 16 to have more than two workpiece contours 20.In the course of the optimization method 10, it can be provided that the workpiece contour 20 eof the workpiece 16 eis segmented or divided into contour segments 28 a- d. For reasons of clarity, the workpiece contour 20 eis divided only into four contour segments 28 a- d. In addition, further contour segments 28 can be produced.The sequence of the optimization method 10 can provide that the contour segment 28 dis determined as a monitoring segment 32 a. During the determination, the contour segments 28 a- dmay be compared with predetermined reference segments 36 a- d. For reasons of clarity, only four reference segments 36 are shown in FIG. 2.For example, the comparison can comprise a geometric comparison between the contour segments 28 a- dand the reference segments 36 a- d, wherein a geometric similarity between the contour segment 28 dand the reference segment 36 dis recognized. The reference segment 36 dis typically known from previous machining methods in conjunction with mismachined and / or machining with a lack of machining quality.The sequence of the optimization method 10 can provide that the predetermined machining parameters 12 of the laser machining machine 14 are compared with the optimum machining parameters 38 dstored for the reference segment 36 d, in order to avoid a possible mismachined of the workpiece 16 e.Furthermore, the sequence of the optimization method 10 can provide that the optimum machining parameters 38 dare transmitted, in particular automatically, to the laser machining machine 14. The machining of the workpiece 16 emay be carried out, at least in the section of contour segment 28 d, using the optimum machining parameters 38 d.FIG. 3 shows a schematic illustration of the panel blank 24 from FIG. 2.The sequence of the optimization method 10 can provide that a machining error 34 occurs during the machining of the panel blank 24 with the laser machining machine 14 (see FIG. 2 ). Typically, the machining error 34 is detected by the laser machining machine 14 and can be transmitted to the optimization system 46 (see FIG. 2 ).The optimization method 10 can provide that the contour segment 28 of a workpiece 16, here contour segment 28 eof the workpiece 16 a, which contour segment can be assigned to a defect position 39 of the machining defect 34, is determined as a monitoring segment 32 b.The optimization method 10 can furthermore provide that the contour segments 28 are checked for similarity to the contour segment 28 e. The check can comprise the contour segments 28 of the same workpiece 16 or of further workpieces 16. A check can show that the contour segment 28 fof the same workpiece 16 aand the contour segments 28 gand 28 hof the further workpiece 16 bare determined to be similar.The contour segments 28 f- hcan thus be determined as monitoring segments 32 c- e. Typically, optimum machining parameters 38 (see FIG. 2 ) are determined for the monitoring segments 32 b- e, which machining parameters make machining possible without machining errors 34. The optimum machining parameters 38 can be determined, for example, by reading out machining parameters 38 stored for a similar reference segment 36 (see FIG. 2 ).List of reference characters10 Optimization method; 12 Machining parameters; 14 Laser machining machine; 16; 16a-h Workpiece; 18 Method step; 20; 20a-k Workpiece contour; 22 Production plan; 24 Panel blank; 26 Method step; 28; 28a-h Contour segment; 29 Workpiece segment; 30 Method step; 32; 32a-e Monitoring segment; 34 Machining errors; 36; 36a-d Reference segment; 38; 38a-d Optimal machining parameters; 39 Error position; 40 Method step; 42 Method step; 44 Laser machining system; 46 Optimization system; 48 Evaluation unit; 50 Output unit; 52 Machine controller.References included in the specificationThis list of documents cited by the applicant has been produced in an automated manner and is only included for the better information of the reader. The list is not part of the German patent application or utility model application. The DPMA does not take any adhesion for any faults or omissions.Patent Literature citedDE 10 2017 105 224 A1

[0004]

Claims

Optimization method (10) for adapting machining parameters (12) in a laser machining method for machining at least one workpiece (16; 16a-h) using a laser machining machine (14), having the method steps: a) providing (18) at least one workpiece contour (20; 20a-k) of the workpiece (16; 16a-h) to be manufactured; b) segmenting (26) the workpiece contour (20; 20a-k) by dividing the at least one workpiece contour (20; 20a-k) into contour segments (28; 28a-h); c) determining (30) at least one monitoring segment (32; 32a-e) in the workpiece contour (20; 20a-k) from the contour segments (28; 28a-h); d) determining (40) predetermined optimum machining parameters (38; 38a-d) to the monitoring segment (32; 32a-e) by comparing the monitoring segment (32; 32a-e) with stored reference segments (36; 36a-d); e) outputting (42) the determined optimum machining parameters (38; 38a-d).Optimization method (10) according to Claim 1, wherein in method step a) a provision of a plurality of workpiece contours (20; 20a-k) of the workpiece (16; 16a-h) to be produced and / or the provision of at least one workpiece contour (20; 20a-k) of a plurality of workpieces (16; 16a-h) to be produced takes place.Optimization method (10) according to Claim 2, wherein in method step b) the segmenting of at least two workpiece contours (20; 20a-k) of an individual workpiece (16; 16a-h) and / or of at least one workpiece contour (20; 20a-k) of at least two different workpieces (16; 16a-h) takes place.Optimization method (10) according to one of the preceding claims, wherein the segmenting of the workpiece contour (20; 20a-k) takes place as a function of a geometric complexity, in particular a change in the contour curvature and / or a change in the contour profile.The optimization method (10) according to one of the preceding claims, wherein in method step c) the determination of the monitoring segment (32; 32a-e) takes place by assigning an error position (39) of a machining error (34) of the laser machining machine (14).Optimization method (10) according to one of the preceding claims, wherein in method step c) the monitoring segment (32; 32a-e) is determined by comparing the contour segments (28; 28a-h) with the stored reference segments (36; 36a-d).Optimization method (10) according to one of the preceding claims, wherein in method step b) each workpiece contour (20; 20a-k) of each workpiece (16; 16a-h) to be produced of a provided production order is segmented, wherein in method step c) monitoring segments (32; 32a-e) are determined in all contour segments (28; 28a-h).The optimization method (10) according to one of the preceding claims, wherein the predetermined machining parameters (12) of the laser machining machine (14) are replaced by the optimum machining parameters (38; 38a-d) at least for the machining of at least one monitoring segment (32; 32a-e).Optimization method (10) according to one of the preceding claims, wherein the method steps b), c) and d) are carried out by an algorithm, in particular a self-learning algorithm.Optimization system (46) for carrying out the optimization method (10) according to one of the preceding claims, comprising - an evaluation unit (48) for segmenting provided workpiece contours (20; 20a-k), for determining monitoring segments (32; 32a-e) and for determining optimum machining parameters (38; 38a-d); - an output unit (50) for outputting the optimum machining parameters (38; 38a-d).The optimization system (46) according to claim 10, wherein the evaluation unit (48) comprises a self-learning algorithm for performing the method steps b), c) and d) of the optimization method (10).Laser processing system (44) for processing workpieces (16; 16a-h), comprising a laser processing machine (14) and an optimization system (46) according to one of claims 10 or 11.

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