MANUFACTURING SYSTEM AND METHOD FOR BOXING SUB-SPACES FOR CONTROLLING A CUTTING PROCESS

Description

[0001] The present invention relates to nesting workpieces to be cut with a flatbed machine tool on a source sheet, in particular to a method for nesting workpieces into subspaces corresponding to the workpieces for controlling a cutting process of a flatbed machine tool, e.g., a flatbed laser cutting machine. Furthermore, the invention relates to a manufacturing system with a flatbed machine tool.

[0002] Nesting refers to the positioning of workpieces on a raw material sheet (as the starting sheet for a laser cutting process). In the sheet metal processing industry, nesting workpieces to be cut on a raw material sheet is part of the production process, whereby nesting results in a specific layout of cutting lines. Herein, the result of a nesting process (the nesting) in the planning and cutting phase is also referred to as a nesting plan, according to which the workpieces are cut from the raw material sheet along the cutting lines defined therein.

[0003] When planning laser cutting processes, a geometric nesting can be performed first. Quality parameters such as a residual material web width (i.e., the minimum width of material that must remain as residual material between two workpieces) can be specified for this. Based on the calculated geometric nesting, a movement sequence, particularly the best possible one, can then be determined for the machining head providing the laser beam for this calculated geometric nesting with respect to the workpieces to be cut. However, it has been recognized that all known nesting results can always be further improved, and existing methods have not always been able to achieve the best nesting.

[0004] From S. UMAR SHERIF ET AL: "Sequential optimization approach for nesting and cutting sequence in laser cutting", JOURNAL OF MANUFACTURING SYSTEMS., Vol. 33, No. 4, October 1, 2014, pages 624-638, a method for generating a nest and a cutting plan for the nest is known.

[0005] One aspect of this disclosure is based on the object of providing an improved nesting process for workpieces to be cut. Another aspect of this disclosure is based on the object of improving process efficiency, in particular material efficiency and / or time efficiency, for a cutting process based on a correspondingly nested arrangement of subspaces and a travel path underlying the nesting, which includes the contours of the subspaces and the connecting paths between the contours of the subspaces.

[0006] At least one of these objects is achieved by a method for nesting subspaces for controlling a cutting process with a flatbed machine tool according to claim 1 and by a manufacturing system according to claim 9. Further developments are specified in the subclaims.

[0007] In a further aspect, a manufacturing system comprises a flatbed machine tool for cutting workpieces from a material sheet according to workpiece-specific cutting contours, and a remote data processing system and / or a local production control system. These are configured to execute the method outlined above to determine an arrangement of subspaces. Furthermore, they are configured to output the arrangement of subspaces as a control signal to the flatbed machine tool in order to cut workpieces corresponding to the subspaces from the material sheet.

[0008] In some embodiments, the evaluation criteria can be at least partially specified by a user. Alternatively or additionally, they can be at least partially specified by a controller, in particular a higher-level controller. The evaluation criteria can include variables associated with the arrangement of subspaces. The variables can characterize an overlap of subspaces, relative angles of sections of the contour lines that can trigger a beam break during the cutting process, and / or a time duration associated with the cutting process, particularly taking heat effects into account. The evaluation data can represent evaluations of the variables associated with the evaluation criteria.

[0009] The methods and systems disclosed herein allow for improved parameters such as material efficiency, order throughput time, and quality to be improved more flexibly than before. In particular, they demonstrate a high degree of flexibility in adapting to different nesting approaches.

[0010] In some embodiments, the result data includes data that defines an improvement parameter, in particular a material efficiency of the arrangement of subspaces, and a quality of workpiece production. Combinatorial logic may include steps in which a comparison, a difference calculation, a ratio calculation, and / or a correlation calculation of the evaluation data with the evaluation criteria are performed.

[0011] In some embodiments, an evaluation algorithm may comprise one or more data aggregation routines. The evaluation algorithm may be configured to activate the data aggregation routine(s) multiple times, in particular more frequently than ten times, in particular more frequently than one hundred times, in particular more frequently than a thousand times, in particular more frequently than ten thousand times, and in doing so, at least once assign output data from another data aggregation routine or from the same data aggregation routine that was previously run as input data to the data aggregation routine. The evaluation algorithm may further be configured to always activate the data aggregation routine(s) with accounting data and one of the other data types as input data. Furthermore, the data aggregation routine may be configured to link, in particular to multiply, the accounting data with one of the other input data.

[0012] In some embodiments, the calculation data is assigned to specific input data of the data aggregation routines. They can be linked, in particular multiplied, with these input data. The calculation data can comprise data designed to improve the arrangement of nested subspaces, in particular the associated evaluation and / or result data. The calculation data can comprise, in particular, data that allows determining which input data has an effect, in particular a positive one, on the result data and / or evaluation data. In particular, the calculation data assigned to these input data can then be set such that the linking, in particular multiplication, of the calculation data assigned to the input data results in the output data influencing the result, in particular as positively as possible, whereby the result can be successively improved.Optionally, the influence of the allocation data on other evaluation data and / or result data can be determined.

[0013] The method and / or device can be used particularly advantageously in industrial production with computer-based production control for processing flat, particularly bending-resistant objects, especially sheet metal parts. Such nesting methods are particularly advantageous for such objects because the evaluation can depend on a large number of criteria. These criteria can sometimes influence each other and are therefore difficult to calculate using automated methods. Examples of such bending-resistant objects include sheets made of metal, glass, plastic, and coated materials such as coated panels made of plastic, wood, metal, glass, etc.

[0014] The method and / or the device can be used particularly advantageously in industrial production with a laser processing machine, in particular a laser cutting machine.

[0015] It is particularly advantageous if production control is also based at least partially on a remote data processing system. Then, parameters, especially weighted variables, can be used to modify, or even improve, the algorithms from one production facility to other production facilities, and vice versa. This provides a much larger database, and the nesting results for each individual production facility can be significantly improved.

[0016] Disclosed herein are concepts that allow aspects of the prior art to be improved, at least in part. In particular, further features and their usefulness will become apparent from the following description of embodiments with reference to the figures. The figures show: Fig. 1 shows a schematic spatial representation of a flatbed machine tool, Fig. 2 shows a schematic representation of a nesting plan and Fig. 3 shows a flow chart of a method for generating a nesting of subspaces for controlling a cutting process of a flatbed machine tool.

[0017] The two-dimensional (2D) nesting problems described herein can be solved, particularly with flatbed laser cutting machines, with a view to reducing the amount of raw material used, since the raw material typically accounts for a significant portion of the overall costs. However, other aspects can also be considered. For example, with flatbed laser cutting machines, the relative positions of the produced workpieces to the support webs of the laser cutting machine can be taken into account in processes. This is disclosed, for example, in the applicant's German patent application 10 2018 124 146 entitled "NESTING WORKPIECES FOR CUTTING PROCESSES OF A FLACHBED MACHINE TOOL" (filed on September 26, 2018), and the German patent application entitled "Evaluating Workpiece Layers in Nested Arrangements" (filed on the same date as the present application).

[0018] Furthermore, a collision check can be performed between the cutting line and the support point tips, as disclosed in EP 2 029 313 B1. Furthermore, a workpiece support with positionable support point tips can be performed, as disclosed in EP 2 029 316 B1. Nesting approaches can be used, as disclosed in "The geometry of nesting problems: A tutorial," by J.A. Bennell et al., European Journal of Operational Research 184 (2008) 397-415.

[0019] With a calculated geometric nesting, a movement sequence, especially the best possible one, can be determined with regard to the workpieces to be cut out, whereby improvements can be made, for example, using traveling salesman approaches.

[0020] The aspects described herein are based in part on the realization that when nesting subspaces in the planning space (corresponding to the arrangement of workpieces on a plate from which the workpieces are to be cut), the efficiency of the underlying cutting process can be incorporated into the evaluation process, particularly in the generation of new nests or underlying sequences of subspaces. The process can be continued until criteria such as material efficiency are met.

[0021] With the methods proposed here, round-tripping between the nesting of sub-spaces and an improvement in the cutting process, for example an improvement in the travel path, can be implemented during the evaluation. Travel path here refers to the path that the laser head travels relative to the material sheet between cutting processes. This can be carried out, for example, as follows: In a first step, a nesting result is generated, whereby instructions for the arrangement of the workpieces (e.g. bottom-left placement) are already applied in order to create initial nesting results with sufficient quality. In the first step, configuration parameters such as the width of the residual material web, i.e. the width of material that should remain as residual material between two workpieces, can be specified; however, this is optional. In a second step, the quality of a nesting result is assessed. For example,Reference points of the subspaces, such as penetration points or pressure points, represent initial data that can be used to evaluate the travel path. The reference points can be represented, for example, as part of a polygon or polygon-like line to illustrate the cutting process. Other factors that can be taken into account include overlaps of subspaces / workpieces, angles at which a beam break is to be expected during a cutting process, or the time required for laser cutting, taking heat effects into account. The evaluation result is used to create a new nest. Subsequently, another evaluation is performed, and the process continues until the specified parameters, such as material efficiency and cut quality, are met.

[0022] In the following, the production of workpieces with a flatbed laser cutting machine is first explained and related to nesting plans ( Figuren 1 und 2 ). A method for generating a nesting plan is then described in conjunction with Fig. 3 described.

[0023] One in Fig. 1 The schematic flatbed machine tool 1 shown comprises a main housing 3 in which the cutting process is carried out using a laser beam. In particular, a focus of the laser beam is guided by a control system along predetermined cutting lines arranged in a processing area over a material in order to cut workpieces with specific shapes from, for example, a substantially two-dimensionally extending sheet metal.

[0024] Furthermore, the flatbed machine tool 1 comprises a pallet changer 5. The pallet changer 5 is designed to position one or more pallets during production. A material sheet to be cut (as raw or starting material) can be stored on a pallet 5A and inserted into the main housing 3 for the cutting process. After the cutting process is completed, the pallet 5A can be removed, as shown in Fig. 1 shown, with a cut material panel 7 out of the main housing 3 so that the cut workpieces 9 can be sorted.

[0025] In the main housing 3, the laser processing head, from which the laser beam emerges, can be freely positioned within the processing area, allowing the laser beam to be guided across the material sheet to be cut essentially along any two-dimensional cutting line. During laser cutting, the laser beam heats the metal along the cutting line until it melts. A gas jet, usually nitrogen or oxygen, typically exits the laser processing head in the area of ​​the laser beam and pushes the molten material downward and out of the resulting gap. The material sheet 7 is thus completely severed by the laser beam during cutting.

[0026] To cut out a workpiece 9, the laser beam is moved along a cutting line 10. This usually starts at a piercing point E (see Fig. 2 ), which may lie outside the workpiece 9, and then approaches the contour of the workpiece 9, in particular in an arc (the so-called lead A). The point at which the cutting line first touches the contour of the workpiece is the point at which the cut is later completed (assuming a continuous cutting process). This point is referred to as pressure point D, as it is the point at which the escaping gas jet exerts pressure on the cut part, namely at the time when it can first move freely. Particularly in the case of thin material sheets, the gas pressure can lead to a tilting of the workpiece, with the result that part of the workpiece can potentially protrude from the plane of the metal sheet and, for example, collide with the cutting head.

[0027] Insertion point E and pressure point D represent reference points of a sub-area in the planning area.

[0028] In Fig. 1 In the embodiment shown, the pallet 5A has a plurality of support webs 11 running transversely to the insertion direction and aligned parallel to one another. For example, the support webs 11 are spaced apart from one another by 20 mm to 100 mm, e.g., 60 mm. The support webs 11 form support areas 11A on which the material sheet 7 is placed. The support areas 11A typically form grid points that can have a spacing of 5 mm to 50 mm, e.g., 15 mm, along the support webs 11, with a support web having a thickness of 1 mm to 5 mm, e.g., 2 mm. The support areas 11A thus form a grid of areas that can influence the cutting process of the material sheet 7 lying on them. The areas of the support webs that influence the cutting process can also extend to areas that are directly adjacent to the support areas in contact with the material sheet, e.g.,the flanks of the support webs 11 leading to the support areas 11A.

[0029] The support webs 11 and in particular the support areas 11A can be taken into account in the arrangement of sub-spaces 9A-9F (corresponding to the workpieces 9) in a planning space 23 (corresponding to the material table 7), for example to increase the quality of cut workpieces 9 or to reduce damage to the support webs 11.

[0030] Fig. 1 further shows a camera 13, which is arranged, for example, on the main housing 3. The camera 13 can be designed, among other things, for image capture of the pallet 5A, the support webs 11 and support areas 11A, as well as the relative position of the material sheet 7 with respect to the pallet 5A (and possibly the support webs 11 and support areas 11A) and is connected to an image evaluation unit of the control system of the flatbed machine tool 1.

[0031] The flatbed machine tool 1 can have a local and / or cloud-based production control system for implementing the methods described herein. This system can be used to generate the evaluations and nestings in real time during operation of the flatbed machine tool 1. This makes it possible, for example, to provide the flatbed machine tool with an improved nesting plan for a cutting process, particularly shortly after the material sheet has been deposited.

[0032] In this document, "cloud-based" and "off-site data processing system" refer to a storage and / or data processing device, particularly a geographically remote, preferably anonymized one, in which data and evaluations from more than one, advantageously several hundred or several thousand different users can be stored and / or processed. This allows different users to contribute to improving the process regardless of the production location. It has been recognized that the described methods only achieve significant success when several hundred, especially several thousand, especially tens of thousands, especially several hundred thousand user evaluations have been read. Such a data volume is often unattainable for a single production facility in a single year. Accordingly, the method would likely have remained uninteresting for such a production facility under certain circumstances.

[0033] It can be seen that cutting paths over webs or the tilting of a workpiece 9 lead to damage to the support web 11 and / or the workpiece 9 and / or a cutting head, thus increasing the risk of scrap and downtime. Furthermore, damage to the support webs 11 leads to higher service costs for replacement or to longer downtimes. If the support web 11 in the storage area 11A is worn away, the number of support points can also be reduced, which can increase the risk of workpieces 9 tilting. If the cutting line 10 runs close to a support area 11A, there is an increased risk of a reduction in quality, e.g. of the underside of the workpiece. The cutting process can also collapse if molten material cannot be sufficiently blown out of the gap and thus a workpiece is not completely separated, which can lead to more scrap parts.

[0034] One task of the nesting methods described herein is therefore also to propose an arrangement of the sub-spaces 9A-9F in the planning space 23 (corresponding to the workpieces 9 in the material table 7) that reduces the previously listed risks and possible additional costs due, for example, to reject parts as well as service and failure of the flatbed machine tool.

[0035] Fig. 2 shows a nesting plan 21 (generally an arrangement of subspaces 9A-9F), as it can be generated with an arrangement rule in a two-dimensional planning space 23 using an algorithm. This can be, for example, a bottom (main direction)-left (secondary direction) arrangement rule. Fig. 2 The main direction runs from right to left, and the secondary direction from top to bottom. The planning space 23 is spanned in the area of ​​a planning table 25. The planning table 25 is transferred to the material sheet 7 for the cutting process in such a way that the corresponding geometric data of the planning space 23 correspond to the processing area provided by the flatbed machine tool 1 for a material sheet 7 (and the bearings correspond to the assumed position data of the support spaces). In the present example, a rectangular planning space 23 has been assumed, which is to be applied to a correspondingly rectangular-shaped material sheet 7. Other shapes are also conceivable.

[0036] The nesting plan 21 is created in a planning phase prior to the cutting process, for example, with the control unit of the flatbed machine tool, if, for example, currently recorded position data is incorporated into the planning, or in a standalone planning unit with appropriate computing capacity, if, for example, predetermined position data is assumed and then subsequently implemented for the cutting process based on the appropriate positioning of the material sheet on the pallet and the support bars. The creation of the nesting plan 21 can be part of the production control system.

[0037] Nesting plan 21 shows a non-overlapping arrangement of subspaces 9A-9F in two-dimensional planning space 23, with the (also two-dimensional) subspaces 9A-9F corresponding to six different types of workpieces. Nesting plan 21 relates to the generation of, here, 50 test workpieces, as an example.

[0038] The illustrated arrangement is based on an insertion sequence for the total of 50 subspaces. An insertion sequence generally determines the order in which the subspaces are inserted successively into the planning space 23 during a sequential generation of the nesting plan 21 and, in this example, are arranged in the planning space 23 according to the bottom-left arrangement rule (strategy).

[0039] The nesting plan 21 also schematically depicts a spatial arrangement of predetermined support spaces 27. To clarify the origin of the support spaces, some of them are grouped together in lines, similar to the support webs, and are shown as points only in the area of ​​subspaces 9A-9F. As already mentioned, position data can be used, in particular, when evaluating the positions of subspaces 9A-9F.

[0040] For clarity, further cutting process data and parameters are shown in an enlarged section of a corner of the planning space 23. Each of the subspaces 9A-9F is delimited by one or more, in particular closed, contours. For example, an outer contour 31A and an inner contour 31B are drawn for subspace 9A (highlighted by dashed lines). For an outer contour 31A' of subspace 9B, a penetration point E, an approach path A, and a pressure point D are also drawn.

[0041] The exemplary subspaces have different sizes, but are all small enough so that the gas pressure, caused for example by the laser cutting nozzle, can influence the stability of a subspace. In the enlarged area of ​​the Fig. 2 one can see individual support spaces 27 within the subspace 9B, which together with the respective pressure point D and the respective center of gravity S of a subspace define a susceptibility to tipping of a workpiece determined by the subspace 9B.

[0042] The one in Fig. 2 The nesting plan 21 shown is also based on a minimum workpiece spacing, which is determined, among other things, by the material thickness of the sheet to be cut and the cutting parameters to be applied. The minimum workpiece spacing can be present at least between two adjacent subspaces 9A-9F arranged in the planning space 23. It can, for example, be in a range of 5 mm to 20 mm, in particular 10 mm.

[0043] For the nesting methods described below, general arrangement rules can be used that define how subspaces are spatially arranged sequentially in the planning space. This is not limited to a bottom-left strategy, as is the basis for the example nesting plans shown in the figures. Rather, bottom-left fill strategies or no-fit polygon approaches, for example, can also be pursued.

[0044] With reference to the Fig. 3 The flowchart shown of a method 39 for nesting subspaces 9A-9F for controlling a cutting process of a flatbed machine tool 1 for cutting out workpieces 9 from a material sheet 7 generally assumes that evaluation criteria exist for nesting that can be implemented based on the representation of subspaces in the planning space. Evaluation criteria relate, for example, to overlaps of geometries, in particular of the subspaces, but also possibly support spaces that correspond to the storage of the material sheet. Evaluation criteria can also relate to angles with beam break-off or the time required for laser cutting, taking heat effects into account. For implementable evaluation criteria, see, for example, the German patent application cited above with the application number 10 2018 124 146 and the title "Evaluation of Workpiece Layers in Nested Arrangements."

[0045] In a first step 41, evaluation criteria for a nesting are read in as a basis for the evaluation of nesting results.

[0046] In a step 43, sequence data 43' for a nesting are generated. Exemplary methods for this are also described in the aforementioned German patent application with application number 10 2018 124 146 and the title "Evaluating Workpiece Layers in Nested Arrangements." Sequence data includes data that describe a complete nesting sequence. This includes, on the one hand, data that defines the contours of the subspaces, i.e., the workpiece shape. Furthermore, sequence data includes reference points of the subspaces, a sequence of cutting operations to be performed for an individual subspace and for all subspaces, as well as connecting paths between the cutting operations.

[0047] In a step 45, the sequence data 43' are evaluated within the framework of an evaluation algorithm 44, and evaluation data for the sequence data 43' are generated. The evaluation data refer to the read-in evaluation criteria.

[0048] By, for example, comparing, subtracting, calculating ratios, calculating correlations, or generally by combinatorial logic, the evaluation data with the evaluation criteria, result data 47' is generated in step 47. The result data 47' can, for example, relate to improvement parameters such as material efficiency, workpiece quality, and damage rate.

[0049] In a step 49, the evaluation algorithm 44 generates allocation data from the sequence data 43', evaluation data, and result data 47'. Allocation data is data that serves to improve the result of the nesting (i.e., the evaluation and / or result data). Allocation data is incorporated into the aggregation, generally understood herein as a recompilation of data sets using aggregation routines, as will be explained below for the evaluation algorithm 44.

[0050] Taking the calculation data into account, new sequence data 43" are generated in a step 51 using the evaluation algorithm 44. (It is noted that step 43 for generating the (initial) sequence data 43' and / or step 47, i.e., the generation of result data, can already be implemented as part of the evaluation algorithm 44.) The calculation data can, for example, be used to generate a new sequence of the subspaces 9A, ... to be arranged, set new priorities for the evaluation, or prefer a specific sequence of the cutting processes of individual or all subspaces 9A, ...

[0051] The algorithm 44, which comprises, for example, steps 45, 49, and 51, activates the execution of at least one data aggregation routine when generating evaluation data, settlement data, and / or further sequence data 43," which aggregates multiple input data items into output data. Data aggregation routines can themselves represent small algorithms / subroutines that link multiple data items from multiple data types. Examples of input data include, for example, the sequence data read in by the evaluation algorithm, the evaluation data generated by the evaluation algorithm, the settlement data generated by the evaluation algorithm, the output data of the same data aggregation routine, and / or the output data of another data aggregation routine. The input data comprises at least two of the aforementioned data types.In some embodiments, data aggregation routines can always run with the allocation data and at least one of the other mentioned input data types. The output data are, for example, the sequence data generated by the evaluation algorithm, the evaluation data generated by the evaluation algorithm, and the allocation data generated by the evaluation algorithm.

[0052] In the evaluation algorithm 44, allocation data can be assigned to certain other input data of the data aggregation routines and linked, in particular multiplied, with these. This makes it possible to determine which input data can have an effect, in particular a positive one, on the result data and / or evaluation data. If the allocation data assigned to this input data is then set such that the linking (multiplication) of the allocation data assigned to the input data results in the output data influencing the result, in particular as positively as possible, the result can be successively improved. At the same time, however, their influence on other evaluation data and / or result data can also be determined and, if necessary, adjusted accordingly.

[0053] As in Fig. 3As indicated, steps 45 to 51 are repeated several times and, accordingly, a plurality of result data 47', 47" is generated for a plurality of sequence data 43', 43". This can be continued until the result data exceeds a range or a maximum number specified with respect to the evaluation criteria.

[0054] From the sequence data 43" generated up to that point, an arrangement of subspaces to be implemented for the cutting process and implementation of the cutting sequence as a result of the method for nesting subspaces are then derived in a step 53. This includes the cutting plan 25, which is to be used as the basis for controlling the flatbed machine tool to produce the workpieces corresponding to the subspaces.

[0055] In other words, the procedures described here can be used to generate nesting results using a genetic algorithm. The efficiency of a nesting result can then be evaluated using a neural network, and the next evolutionary stage of the genetic algorithm can be initiated based on this. The process ends when predefined criteria (e.g., material efficiency) are met. This allows round-tripping between nesting and travel path improvement / optimization. This can be implemented, for example, as follows: In a first step, a genetic sequence is generated that represents a nesting result. For this purpose, (arrangement) rules (e.g., bottom-left placement) are applied to create initial nesting results with sufficient quality. In the first step, configuration parameters such as web width can be specified, but this is purely optional.

[0056] In a second step, neural networks (e.g., Kohonen feature maps) are used to assess the quality of a nesting result. The output neurons can be represented by the reference points of the parts (e.g., represented as a polygon), with the connections of the output neurons determined by anomalies (e.g., overlaps of geometries, angles of beam break-off, or the time required for laser cutting, taking heat effects into account).

[0057] In a third step, the output of the neural network is used to resequence the gene sequence, which is then used in the genetic algorithm. Subsequently, the neural network performs another classification. This process continues until the specified optimization parameters (such as material efficiency, quality, etc.) are met.

Claims

1. A method (39) for nesting sub-spaces (9A-9F), wherein the sub-spaces (9A-9F) are arranged in a two-dimensional planning space (23) and are delimited by contour lines in the two-dimensional planning space (23), and wherein a sub-space (9A-9F) is provided for controlling a cutting process of a flatbed machine tool (1) for cutting out a workpiece (9) from a material panel (7) along at least one cutting line that corresponds to a contour line of the sub-space (9A-9F), with the following steps that are carried out by a remote data processing system and / or a local manufacturing control system: a. reading in (step 41) evaluation criteria for an arrangement of nested sub-spaces (9A-9F); b. generating (step 43) sequence data (43') for the arrangement of nested sub-spaces (9A-9F), wherein the sequence data (43', 43") comprise data that describe a nesting sequence of the sub-spaces in the planning space (23), said nesting sequence comprising a predefined number of sub-spaces (9A-9F), wherein the data comprise contour lines of the sub-spaces (9A-9F), a cutting order of the contour lines of the sub-spaces (9A-9F), reference points of the sub-spaces (9A-9F), and connection paths between the contour lines; c. generating (step 45) evaluation data by evaluating the sequence data (43') using an evaluation algorithm (44); d. generating (step 47) result data (47') using the evaluation criteria based on a combinatorial logic of the evaluation data; e. generating (step 49) calculation data from the sequence data (43'), evaluation data, and result data (47') using the evaluation algorithm (44); f. generating (step 51) further sequence data (43"), taking into account the calculation data, using the evaluation algorithm (44); and g. repeating the steps c. through f. until the result data (47', 47") exceeds a predefined range, h. deriving (step 53), from the sequence data (43") generated up to this point, an arrangement of sub-spaces to be implemented for a cutting process, and implementing the cutting series as the result of the method for nesting sub-spaces, i. outputting the derived arrangement of sub-spaces as a control signal to the flatbed machine tool (1) in order to cut out from the material panel (7) workpieces that correspond to the sub-spaces (9A-9F), wherein the evaluation algorithm (44) executes at least one data aggregation routine during the generation of evaluation data, calculation data, and / or further sequence data (43"), said routine, i. when activated, aggregating multiple input data to form output data, ii. wherein the input data have at least two of the following data types: the sequence data (43', 43") read in by the evaluation algorithm, the evaluation data generated by the evaluation algorithm, the calculation data generated by the evaluation algorithm, the output data of another data aggregation routine, and the output data of the same data aggregation routine that was previously run through, iii. wherein the output data influence at least one of the following data types: the sequence data (43") generated by the evaluation algorithm, the evaluation data generated by the evaluation algorithm, and the calculation data generated by the evaluation algorithm.

2. The method (39) of claim 1, wherein the evaluation criteria comprise variables associated with the arrangement of sub-spaces, and wherein the variables characterize an overlapping of sub-spaces (9A-9F), relative angles of sections of the contour lines that can trigger a breaking off of the beam during the cutting process, and / or a time duration to be associated with the cutting process, in particular one that takes into account heat effects, and wherein the evaluation criteria are at least partially predefinable by a user or are alternatively or additionally predefinable, at least partly, by a control system, in particular a superordinate control system.

3. The method (39) according to claim 1 or claim 2, wherein the evaluation data represents evaluations of the variables that are associated with the evaluation criteria.

4. The method (39) according to one of the preceding claims, wherein the result data comprise data that define an improvement parameter, in particular a material efficiency of the arrangement of sub-spaces (9A-9F), and a quality of the workpiece generation, and wherein the combinatorial logic comprises steps that carry out a comparison, a formation of a difference, a formation of a ratio, and / or a formation of a correlation of the evaluation data with the evaluation criteria.

5. The method (39) according to one of the preceding claims, wherein the calculation data are associated with certain input data of the data aggregation routines, and are linked thereto, in particular multiplied, and wherein the calculation data comprise data that are designed to improve the arrangement of nested sub-spaces (9A-9F), in particular to improve the associated evaluation and / or result data (47', 47").

6. The method (39) according to claim 5, wherein the calculation data comprise data that can be employed for a determining of which of the input data have an effect, in particular as positive an effect as possible, on the result data and / or evaluation data, in particular in order to then adjust the calculation data associated therewith such that the link, in particular multiplications, of the calculation data associated with the input data results in the output data influencing the result, in particular as positively as possible, whereby the result can be successively improved, and wherein optionally an influence of the calculation data on other evaluation data and / or result data (47', 47") is ascertained.

7. The method (39) according to one of the preceding claims, wherein the evaluation algorithm (44) carries out the at least one data aggregation routine multiple times, in particular more frequently than ten time, in particular more frequently than a hundred times, in particular more frequently than a thousand times, in particular more frequently than ten thousand times, and in the process assigns to the data aggregation routine, at least once, output data of another data aggregation routine or of the same data aggregation routine which had been run through previously.

8. The method (39) according to one of the preceding claims, wherein a data aggregation routine is designed to link multiple data from multiple data types and / or wherein the input data comprise, in particular, the calculation data generated by the evaluation algorithm (44), and in addition the sequence data (43', 43"), the evaluation data, the output data of another data aggregation routine, and / or the output data of the same data aggregation routine and / or wherein the at least one data aggregation routine is conceived to link the calculation data to a different data type, in particular to multiply it.

9. A manufacturing system, with a flatbed machine tool (1) for cutting workpieces (9) out of a material panel (7) according to workpiece-specific cutting contours, and a remote data processing system and / or a local manufacturing control system configured to execute a method (39) according to one of claims 1 to 8 in order to determine an arrangement of sub-spaces (9A-9F), and to issue the arrangement of sub-spaces (9A-9F) as a control signal to the flatbed machine tool (1) in order to cut out from the material panel (7) work pieces that correspond to the sub-spaces (9A-9F).

10. The manufacturing system of claim 9, wherein the remote data processing system comprises a computation, control, and / or storage device, in particular one at a remote location, preferably anonymized, in which the evaluation data and / or the result data (47', 47") are generated and stored for a plurality of sequence data (43', 43"), advantageously from multiple hundreds or multiple thousands of different sequence data for a nesting, in particular from different users, in particular anonymized.

11. The manufacturing system according to claim 9 or claim 10, wherein the flatbed machine tool (1) is configured for the processing of flat, in particular non-bending, objects such as sheet metal panels, and in particular is designed as a laser processing machine such as a laser cutting machine.