Optimisation method for a laser machining method, optimisation system and laser machining system

Abstract

The invention relates to an optimisation method (10) for adjusting machining parameters (12) in a laser machining method for machining at least one workpiece (16; 16a-h) by means of a laser machine tool (14), having the following method steps: f) providing (18) at least one workpiece contour (20; 20a-k) of the workpiece (16; 16a-h) to be produced; g) segmenting (26) the workpiece contour (20; 20a-k) by subdividing the at least one workpiece contour (20; 20a-k) into contour segments (28; 28a-h); h) ascertaining (30) at least one monitoring segment (32; 32a-e) in the workpiece contour (20; 20a-k) from among the contour segments (28; 28a-h); i) determining (40) predefined optimum machining 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 ascertained optimum machining parameters (38; 38a-d). The invention also relates to an optimisation system (46) and to a laser machining system (44).

Description

[0001] Optimization method for a laser processing process, optimization system and laser processing system

[0002] Background of the invention

[0003] The invention relates to an optimization method for a

[0004] Laser processing method. The invention also relates to a

[0005] Optimization system and a laser processing system with a

[0006] Laser processing machine.

[0007] When machining workpieces using laser processing machines, a production order, typically the processing of a sheet blank with a large number of workpieces to be manufactured, is processed using a predetermined set of machining parameters. The machining parameter set usually comprises a combination of various individual machining parameters that have been determined to be particularly suitable for the machining process. Depending on the material to be machined (e.g., steel, aluminum, etc.), and / or the workpiece geometry (e.g., workpiece thickness, workpiece contour, etc.), the operator must select the machining parameter set from a large number of stored machining parameter sets. These sets are prepared in advance of the machining process using complex and costly test series.If an unsuitable set of machining parameters is selected by the operator, this leads to reduced machining quality, machining errors and / or decreased productivity.

[0008] Furthermore, even an optimally selected set of machining parameters represents a compromise when machining the sheet blank. Typically, a sheet blank comprises, for example, different workpiece geometries and material inhomogeneities, so that machining with the predetermined machining parameters is only optimal for a portion of the sheet blank to be machined. Determining multiple machining parameter sets for specific sub-areas or workpieces of the sheet blank significantly increases the preparation effort required by the operator. Furthermore, deviations in material homogeneity can be difficult or even impossible for the operator to detect, meaning that even the selection of different machining parameter sets cannot ensure optimal machining.

[0009] DE 10 2017 105 224 A1 discloses a device for machine learning, a laser processing system, and a machine learning method. To optimize the manufacturing process, state variables during manufacturing, in particular reflected light, are observed and taken into account when adjusting the processing parameters. The previously known device performs optimization without considering the workpiece geometry and material homogeneity, so the aforementioned problems persist.

[0010] Object of the invention

[0011] It is an object of the invention to improve the processing quality of workpieces by means of a laser processing machine, to avoid miscuts and to increase the productivity of the laser processing machine.

[0012] Description of the invention

[0013] The underlying problem is solved according to the invention by an optimization method having the features of patent claim 1. In addition, the problem is solved by an optimization system having the features of patent claim 10. Furthermore, the problem is solved by a laser processing system having the features of patent claim 12. The subclaims give preferred embodiments of the invention.

[0014] According to the invention, an optimization method is provided.

[0015] The optimization method is designed to adapt 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.

[0016] The optimization process includes at least the following process steps:

[0017] In a method step a) of the optimization method, at least one workpiece contour of the workpiece to be manufactured is provided. A workpiece contour is understood to be a self-contained contour of the workpiece. A workpiece contour can be designed as an inner contour or an outer contour of the workpiece. The workpiece contour preferably contains geometric information about the workpiece. Typically, the workpiece contour is provided in the form of machine-readable data and / or in the form of graphic data. This allows the optimization method to be used flexibly.

[0018] 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, one contour segment can be configured in the form of the entire workpiece contour, and further contour segments can be configured as partial sections of the workpiece contour.

[0019] Furthermore, 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 determined from the contour segments. In other words, the contour segments are checked to determine whether a monitoring segment is present. A monitoring segment is understood to be 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 start of machining and / or during machining if a machining error or machining with reduced machining quality is detected.

[0020] A machining error can occur, for example, but not exclusively, as a miscut, a laser beam interruption, a collision between a machining nozzle of the laser machining machine and the sheet blank, and / or as thermal deformation of the sheet blank. A machining error can occur, for example, but not exclusively, as a result of excessive machining speed, excessive machining power, excessively low material quality, especially localized quality, and / or excessive geometric complexity of the workpiece.

[0021] A further method step d) of the optimization process involves determining predetermined optimal machining parameters for the monitored segment by comparing the monitored segment with stored reference segments. In other words, machining parameters are determined that have led to successful or high-quality machining for similar contour segments.

[0022] In addition, in a process step e) of the optimization process, the determined optimal processing parameters are output. The optimal processing parameters can be communicated to a human operator of the laser processing machine, preferably via a display of the

[0023] Laser processing machine. Preferably, the optimal processing parameters are output to a machine control system of the laser processing machine. This allows adjustment of the

[0024] Processing parameters can be determined particularly quickly and automatically. In summary, the invention proposes an optimization method in which the predetermined processing parameters of the laser processing machine can be dynamically adjusted using a segment-based determination of optimal processing parameters. The processing process, in particular a laser cutting process, is continuously monitored, taking into account the geometric complexity with regard to potentially problematic contour segments and processing errors, thus enabling rapid adjustment of the processing parameters. This reduces processing errors and maintains high processing quality.

[0025] In a preferred embodiment of the optimization method, in method step a), several workpiece contours of the workpiece to be manufactured are provided. Typically, at least all workpiece contours of a workpiece to be manufactured are provided. This allows the at least one workpiece to be manufactured to be fully monitored.

[0026] Alternatively or additionally, at least one workpiece contour from several workpieces to be manufactured can be provided. This allows for more effective optimization of the laser processing process.

[0027] A preferred development of the optimization method is one in which, in method step b), at least two workpiece contours of a single workpiece are segmented. Alternatively or additionally, it can be provided that at least one workpiece contour of at least two different workpiece geometries is segmented.

[0028] Preferably, all workpiece contours of all workpieces to be manufactured in a production order are provided. A production order typically comprises the workpieces to be manufactured on a single sheet blank using a predetermined set of machining parameters. This allows the optimization method to be used particularly effectively. A preferred embodiment of the optimization method is one in which the segmentation of the workpiece contour occurs as a function of a geometric complexity, in particular a change in the contour curvature and / or a change in the contour profile. The geometric complexity can be understood as the degree of dynamic change in the machining parameters. Typically, a high degree of complexity requires a larger change in the predetermined machining parameters.Workpieces with high geometric complexity therefore typically exhibit more frequent machining errors and / or machining with reduced machining quality.

[0029] A further preferred embodiment of the optimization method is one in which, in method step c), the monitoring segment is determined by assigning an error position of a processing error of the laser processing machine. In other words, a contour segment can be identified as a monitoring segment by the occurrence of a processing error on the contour segment. This allows the optimization method to be updated, particularly with regard to previously unknown monitoring segments, and further processing errors on similar contour segments can be avoided.

[0030] In a preferred embodiment of the optimization method, the monitoring segment is determined in method step c) by comparing the contour segments with stored reference segments. In other words, problematic contour segments can be identified by comparing them with already known problematic contour segments. This allows the machining parameters to be changed in advance of a machining error, thus preventing a potential machining error.

[0031] A particularly preferred embodiment of the optimization method is one in which, in method step b), each workpiece contour of each workpiece to be manufactured in a provided production order is segmented, with monitoring segments being determined in all contour segments in method step c). This allows the optimization method to be applied particularly effectively to the entire production order.

[0032] In a preferred embodiment of the optimization method, preset processing parameters of the laser processing machine are replaced with the optimal processing parameters, at least for the processing of at least one monitored segment. This can prevent the processing of a problematic contour segment or workpiece section with unsuitable processing parameters.

[0033] In a particular embodiment of the optimization method, the predetermined machining parameters can first be provided and compared with the determined optimal machining parameters. Preferably, only those predetermined machining parameters that deviate from the optimal machining parameters by a predetermined value are subsequently replaced. This allows for adjustment taking the predetermined machining parameters into account.

[0034] Further preferred is an embodiment of the optimization method in which method steps b), c), and d) are performed by an algorithm, in particular a self-learning algorithm. A self-learning algorithm enables particularly fast and effective segmentation of the workpiece contours, determination of monitoring segments, and determination of optimal processing parameters.

[0035] Typically, the algorithm, especially a self-learning algorithm, is trained using a large amount of generated and measured training data before workpieces are processed by the laser processing machine. Training data can be processing parameters, e.g., laser power, processing speed, nozzle-to-workpiece distance, or focus position. Training data can also be sheet blank data, e.g., the material quality of the sheet blank, the material type of the sheet blank, or the material thickness of the sheet blank. Training data can also be manufacturing information, e.g., a workpiece contour, a contour segment, a geometric complexity, a processing error, etc. The training data is typically used in conjunction with the processing result generated by the corresponding training data to train the self-learning algorithm.

[0036] The underlying task is also solved by an optimization system.

[0037] The optimization system is designed to optimize the processing of workpieces by a laser processing machine. Furthermore, the optimization system is configured to perform the optimization method described above and below.

[0038] The optimization system comprises an evaluation unit. The evaluation unit is designed and configured for the segmentation of provided workpiece contours as described above and below, for determining monitoring segments in at least one workpiece contour, and for determining optimal machining parameters.

[0039] The optimization system also includes an output unit for outputting the optimal processing parameters. The output unit can be configured to output the optimal processing parameters graphically and / or acoustically. Alternatively or additionally, the output unit can be configured to output the optimal processing parameters to the laser processing machine.

[0040] In a particular embodiment of the optimization system, the evaluation unit comprises 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 problem is further solved by a laser processing system. The laser processing system comprises at least one laser processing machine for processing workpieces. According to the invention, the laser processing system also comprises an optimization system, described above and below.

[0041] Further advantages of the invention will become apparent from the description and the drawings. Likewise, the above-mentioned and further-described features can be used individually or in combination in any desired manner. The embodiments shown and described are not intended to be exhaustive, but rather are exemplary in nature for describing the invention.

[0042] Detailed description of the invention and drawing

[0043] Fig. 1 shows a schematic representation of an optimization method for adjusting processing parameters in a laser processing process.

[0044] Fig. 2 shows a schematic representation of a laser processing system with an optimization system and a laser processing machine for manufacturing workpieces from a sheet blank.

[0045] Fig. 3 shows a schematic representation of the blank sheet from Fig. 2.

[0046] Fig. 1 schematically shows an optimization method 10 according to the invention. The optimization method 10 is explained in more detail below with reference to the other figures of the drawing.

[0047] The optimization method 10 is suitable and designed for adapting processing parameters 12 in a laser processing method (not shown in detail). Processing parameters 12 typically relate to machine parameters of a laser processing machine 14 (see Fig. 2) with which one or more workpieces 16 (see Figs. 2, 3) are processed while maintaining high processing quality and processing speed. Typical machine parameters of a laser processing machine 14 include, for example, a laser power, a feed rate, and / or a focus position of a processing laser beam (not shown) of the laser processing machine 14.

[0048] The optimization method 10 comprises at least the following process steps:

[0049] 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 manufactured is provided.

[0050] 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 geometric information of the workpieces 16 to be manufactured, in particular information on the workpiece contour 20.

[0051] Alternatively or additionally, the workpiece contour 20 can be provided by transmitting a graphic image. Typically, the workpiece contour 20 can be determined from a graphic image of the workpiece 16 to be manufactured, in particular using graphic evaluation algorithms.

[0052] 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 manufactured and / or a plurality of workpieces 16 to be manufactured. Typically, when transmitting the production plan 22, the geometric information for all workpieces 16 to be manufactured of a sheet blank 24 to be machined (see Figs. 2, 3) is provided, so that preferably all workpiece contours 20 of all workpieces 16 to be manufactured are provided. This allows the optimization method 10 to be used particularly effectively.

[0053] A further method step 26 of the optimization method 10 provides for segmenting the at least one workpiece contour 20. The segmentation comprises subdividing 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. Particularly preferably, all provided workpiece contours 20 of all workpieces 16 to be manufactured of a sheet blank 24 are segmented.

[0054] The segmentation or subdivision of the workpiece contour 20 into contour segments 28 typically comprises an evaluation of the geometric information of the workpiece contours 20. Preferably, the evaluation of the geometric information relates to the determination of a geometric complexity of the workpiece(s) 16 to be manufactured and / or a material inhomogeneity of the sheet blank 24.

[0055] In particular, a change in the contour curvature of the workpiece contour 20 can be taken into account when evaluating the geometry information. A change in the contour curvature typically requires a change in the processing parameters 12 of the laser processing machine 14 in order to maintain high processing accuracy.

[0056] In particular, a change in the contour profile of the workpiece contour 20 can be taken into account when evaluating the geometric information. A change in the contour profile typically indicates a significant change in direction of the workpiece contour 20, which is usually accompanied by a change in the processing parameters 12 of the laser processing machine 14. Preferably, a workpiece surface 29 enclosed by a workpiece contour 20 (see Fig. 2) can be taken into account when evaluating the geometric information. For example, a small workpiece surface 29 can cause a high energy input per unit area during processing, which can lead to deformation of the sheet blank 24.

[0057] Further preferably, with knowledge of 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 related 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.

[0058] In particular, when evaluating the geometry information, a distance between two or more 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 thermal overload or local deformation of the sheet blank 24, so that the processing parameters, in particular the laser power, or the production process must be changed.

[0059] The segmentation or subdivision of the workpiece contour 20 can include 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. This allows the optimization method 10 to be carried out even more precisely.

[0060] A further method step 30 of the optimization method 10 provides for determining 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 processing by the laser processing machine 12 with the set processing parameters 12 can lead to reduced processing quality and / or to a processing error 34 (see Fig. 3).

[0061] A machining error 34 or machining with reduced machining quality may have occurred and been detected during the machining of previous workpieces on a reference segment 36 (see Fig. 2). Typically, the machining parameters 12 in the previous machining process were subsequently corrected to achieve a more optimal machining result. By storing the reference segment 36, preferably in conjunction with optimal 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.

[0062] Alternatively or additionally, the monitoring segment 32 can be determined by assigning the processing error 34 detected during processing by the laser processing machine 12 or a detected processing with reduced processing quality. For example, a miscut by the laser processing machine 14 can be detected by detecting increased process radiation. Such a miscut can be attributed, for example, to a locally deviating material quality of the sheet blank 24, which requires a change in the processing parameters 12.

[0063] The laser processing machine 14 outputs a corresponding error message in conjunction with an error position 39 (see Fig. 3) of the machining error 34 or the position of the machining with reduced machining quality. The contour segment 28 can be designated as a monitoring segment 32 and / or a reference segment 36 by assigning it to the error position 39. This allows the optimization method 10 to prevent the recurrence of the machining error 34 and / or machining with reduced machining quality at similar sections of the workpiece contours 20 during further and / or future machining.

[0064] The optimization method 10 is preferably designed to check all contour segments 28 for possible monitoring segments 32, particularly after the complete segmentation of all workpiece contours 20 of the workpieces 16 to be manufactured. This allows for a particularly comprehensive evaluation and reliably prevents machining errors.

[0065] In particular, the optimization method 10 is designed to determine monitoring segments 32 multiple times, in particular continuously, during processing by the laser processing machine 12. In other words, the method step 30 can be performed multiple times, in particular continuously.

[0066] A further method step 40 of the optimization method 10 provides a

[0067] Determining predetermined optimal processing parameters 38 for the

[0068] Monitoring segment 32. In other words,

[0069] Processing parameters 38 are determined, which allow editing of the

[0070] Monitoring segment 32 with high processing quality and / or

[0071] enable production speed.

[0072] The determination of optimal processing parameters 38 can be carried out by comparing the monitoring segment 32 with stored reference segments 36. Typically, optimal processing parameters 38 are stored for the stored reference segments 36, which have led to optimal processing results in previous processing operations. In other words, processing parameters 12 can be determined for which error-free processing of a workpiece 16 with a similar contour segment 28 or geometry segment is known. To determine the optimal processing parameters 38, it can be provided that the preset processing parameters 12 of the laser processing machine 14 are first provided. Typically, the preset processing parameters 12 can be evaluated from the production plan 22.Subsequently, it can be provided that the preset machining parameters 12 are compared with the optimal machining parameters 38 and subsequently replaced or adjusted. Furthermore, it can be provided that the predetermined machining parameters 12 are brought closer to the optimal machining parameters 38. This allows a compromise to be reached between the predetermined machining parameters 12 and the optimal machining parameters 38.

[0073] A further method step 42 of the optimization method 10 provides for outputting the determined optimal machining parameters 38. The optimal machining parameters 38 can be output graphically.

[0074] Alternatively or additionally, it can be provided that the optimal processing parameters 38 are output to the laser processing machine 14, in particular in the form of machine-readable data.

[0075] Preferably, the output optimal processing parameters 38 on the laser processing machine 14 replace the preset processing parameters 12 at least for the processing of at least one monitoring segment 32.

[0076] In a particular embodiment of the optimization method 10, it can be provided that the method steps 26, 30, 40 are carried out by a particularly self-learning algorithm. This allows a plurality of contour segments 20 of a plurality of workpieces 16 to be checked for monitoring segments 32, and a particularly fast and precise output of optimal processing parameters 38 can be achieved. Fig. 2 shows a schematic representation of a laser processing system 44.

[0077] The laser processing system 44 has a laser processing machine 14 for processing workpieces 16, here the workpieces 16a-16g, by means of a processing laser beam (not shown).

[0078] The laser processing system 44 also has an optimization system 46 that is configured to carry out the optimization method 10 described above and below (see Fig. 1).

[0079] The optimization system 46 has an evaluation unit 48 for segmenting the provided workpiece contours 20. The evaluation unit 48 is also configured to determine monitoring segments 32. Furthermore, the evaluation unit 48 is configured to determine optimal machining parameters 38, in this case optimal machining parameters 38a-d.

[0080] Preferably, the evaluation unit 48 has an algorithm, in particular a self-learning algorithm, for carrying out the method steps 26, 30, 40 (see Fig. 1).

[0081] The optimization system 46 also has an output unit 50 for outputting the optimal processing parameters 38. As shown, the output unit 50 is configured for communication, or data exchange, with a machine control system 52 of the laser processing machine 14. This allows the optimal processing parameters 38 to be transmitted particularly quickly and automatically.

[0082] A sequence of the optimization method 10 can provide that, at the beginning of the production of the workpieces 16a-h, as shown, a production plan 22 is transmitted to the optimization system 46 by the machine control 52 of the laser processing machine 14. The production plan 22 typically contains at least one workpiece contour 20 of a workpiece 16 to be produced, here the workpiece contours 20a-k of the workpieces 16a-h to be produced from the sheet blank 24. The workpieces 16 can be cut from the sheet blank 24, for example, using a processing laser beam of a laser cutting machine.

[0083] The workpieces 16a-e each have a workpiece contour 20a-e. The workpieces 16f-h each have an outer workpiece contour 20f-h and an inner workpiece contour 20i-k. In other words, the workpieces 16f-h have two workpiece contours 20. Furthermore, it is also conceivable that the workpieces 16 may have more than two workpiece contours 20.

[0084] During the optimization process 10, the workpiece contour 20e of the workpiece 16e can be segmented or divided into contour segments 28a-d. For clarity, the workpiece contour 20e is divided into only four contour segments 28a-d. In addition, additional contour segments 28 can be generated.

[0085] The sequence of optimization method 10 may provide for the contour segment 28d to be determined as the monitoring segment 32a. During the determination, the contour segments 28a-d may be compared with predetermined reference segments 36a-d. For reasons of clarity, only four reference segments 36 are shown in Fig. 2.

[0086] For example, the comparison may include a geometric comparison between the contour segments 28a-d and the reference segments 36a-d, wherein a geometric similarity between the contour segment 28d and the reference segment 36d is detected. The reference segment 36d is typically known from previous machining processes in connection with incorrect machining and / or machining operations with poor machining quality. The sequence of the optimization method 10 may provide for the predetermined machining parameters 12 of the laser processing machine 14 to be compared with the optimal machining parameters 38d stored for the reference segment 36d in order to avoid possible incorrect machining of the workpiece 16e.

[0087] Furthermore, the sequence of the optimization method 10 can provide for the optimal processing parameters 38d to be transmitted, in particular automatically, to the laser processing machine 14. The processing of the workpiece 16e can be carried out, at least in the section of the contour segment 28d, with the optimal processing parameters 38d.

[0088] Fig. 3 shows schematically the panel blank 24 from Fig. 2.

[0089] The sequence of the optimization method 10 may provide for a processing error 34 to occur during the processing of the sheet blank 24 with the laser processing machine 14 (see Fig. 2). Typically, the processing error 34 is detected by the laser processing machine 14 and can be transmitted to the optimization system 46 (see Fig. 2).

[0090] The optimization method 10 can provide that the contour segment 28 of a workpiece 16 that can be assigned to an error position 39 of the machining error 34, here contour segment 28e of the workpiece 16a, is determined as a monitoring segment 32b.

[0091] The optimization method 10 can further provide that the contour segments 28 are checked for similarity to the contour segment 28e. The check can include the contour segments 28 of the same workpiece 16 or of further workpieces 16. A check can result in the contour segment 28f of the same workpiece 16a as well as the contour segments 28g and 28h of the further workpiece 16b being determined to be similar. The contour segments 28f-h can thus be determined as monitoring segments 32c-e. Typically, optimal machining parameters 38 (see Fig. 2) are determined for the monitoring segments 32b-e, which enable machining without machining errors 34. The optimal machining parameters 38 can be determined, for example, by reading out

[0092] The processing parameters 38 stored in the reference segment 36 (see Fig. 2) are determined.

[0093]

[0094] 10 optimization methods;

[0095] 12 processing parameters;

[0096] 14 laser processing machine;

[0097] 16; 16a-h workpiece;

[0098] 18 process steps;

[0099] 20; 20a-k workpiece contour;

[0100] 22 Production plan;

[0101] 24 blank panels;

[0102] 26 process steps;

[0103] 28; 28a-h contour segment;

[0104] 29 workpiece segment;

[0105] 30 process steps;

[0106] 32; 32a-e monitoring segment;

[0107] 34 processing errors;

[0108] 36; 36a-d reference segment;

[0109] 38; 38a-d optimal machining parameters;

[0110] 39 error position;

[0111] 40 process steps;

[0112] 42 process steps;

[0113] 44 laser processing system;

[0114] 46 optimization system;

[0115] 48 evaluation unit;

[0116] 50 output units;

[0117] 52 Machine control.

Claims

Patent claims 1. 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: 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 subdividing 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 optimal machining 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); e) outputting (42) the determined optimal machining parameters (38; 38a-d).

2. 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 manufactured and / or the provision of at least one workpiece contour (20; 20a-k) of a plurality of workpieces (16; 16a-h) to be manufactured takes place.

3. Optimization method (10) according to claim 2, wherein in method step b) the segmentation of at least two workpiece contours (20; 20a-k) of a single 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.

4. Optimization method (10) according to one of the preceding claims, wherein the segmentation 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.

5. Optimization method (10) according to one of the preceding claims, wherein in method step c) the determination of the monitoring segment (32; 32a-e) is carried out by assigning an error position (39) of a processing error (34) of the laser processing machine (14).

6. 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).

7. 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 manufactured 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).

8. Optimization method (10) according to one of the preceding claims, wherein the predetermined processing parameters (12) of the laser processing machine (14) are replaced by the optimal processing parameters (38; 38a-d) at least for the processing of at least one monitoring segment (32; 32a-e).

9. 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.

10. Optimization system (46) for performing 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 optimal machining parameters (38; 38a-d); - an output unit (50) for outputting the optimal Processing parameters (38; 38a-d).

11. Optimization system (46) according to claim 10, wherein the evaluation unit (48) comprises a self-learning algorithm for carrying out the method steps b), c) and d) of the 12. A 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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