Panel furniture part cutting layout and central tool path optimization method and system

Abstract

The invention discloses a panel furniture part cutting layout and center tool path optimization method and system. The method comprises the steps that machining parameters are obtained; generating a candidate placement bit set for each part, sorting according to the dictionary sequence key, and sequentially placing all parts to obtain an initial layout; in the dictionary order keys, the first order key is a common edge constraint cost function value; in the part placing process, symmetric seam reserving constraint, shared center line extraction, Tab duplicate removal and layered cutter lifting-free path generation are executed, and a final layout is obtained; and outputting part coordinates of final layout, and sharing a center line tool path and a processing file of Tab arrangement. According to the layout result, the material utilization rate is guaranteed, meanwhile, good machinability and executability are achieved, the number of times of cutting is reduced, meanwhile, the total length of idle movement is controlled, and the predicted machine time is shortened.

Description

TECHNICAL FIELD

[0001] The present application relates to two-dimensional plate material blanking layout and tool path optimization, in particular to a plate furniture part cutting layout and center tool path optimization method and system. BACKGROUND

[0002] In the production mode of mass customization, efficient and low-cost manufacturing and supply of products are the key to enterprise competitiveness. As an important link between the upper and lower parts of the production process, the scheme of blanking layout directly affects material utilization, production cost and processing efficiency. An excellent layout scheme can minimize waste of plate material, reduce handling and cutting time, and improve material utilization. In recent years, various advanced layout algorithms have emerged, mainly divided into two categories: heuristic algorithms and intelligent optimization algorithms. Although intelligent algorithms (such as genetic algorithms, simulated annealing, etc.) can theoretically approach the optimal material utilization rate, excessive pursuit of higher material utilization rate often leads to long calculation time, parameter sensitivity and complex cutting path. In fact, due to cutting knife seams and waste factors, 80%~90% of material utilization rate is already a very ideal layout result. Many seemingly optimal layout layouts are actually difficult to make actual cutting process, reduce processing efficiency and exacerbate tool wear due to too complex tool path and winding tool path. For example, the existing literature compares two rectangular part layout schemes: scheme one cuts each knife as much as possible through multiple part edges through edge cutting, and only 19 knives are needed to cut 17 rectangular parts on the same plate; scheme two does not fully utilize the edge and is insufficient in cutting sequence optimization, and 23 knives are needed to cut 15 rectangular parts. The above examples show that in the case of similar number of rectangular parts, the fewer the number of knives, the less the material loss and the faster the blanking pace. Therefore, under the premise of ensuring material utilization, further reducing the cutting path length and the number of knives has become an important issue in the field of two-dimensional plate blanking optimization.

[0003] Traditional rectangular parts layout method focuses on maximizing the utilization of sheet material, but lacks consideration of the optimization of specific cutting tool path. Guillotine type blanking scheme requires the cutting path to run through the full width of the sheet, although it is easy to achieve edge-to-edge cutting, but it is limited by equipment and process flexibility, and cannot fully adapt to complex part layout. Non-guillotine CNC free cutting method has the advantage of flexible path, but if the edge-to-edge cutting is not planned, there may be a large number of scattered short paths and frequent tool lifting, which reduces the processing efficiency. The existing layout algorithm ignores the overall optimization of the tool path, resulting in reduced blanking efficiency; the existing machining center cutting path planning does not consider the layout at the same time, resulting in the potential of edge-to-edge cutting not being fully utilized. Edge-to-edge cutting refers to arranging parts with the same edge length as much as possible during layout optimization, so that their common edges only need to be cut once. Edge-to-edge cutting can effectively reduce the total cutting path length and cutting times, improve processing efficiency and save energy. In practical applications, edge-to-edge cutting requires that through reasonable path planning, all parts share edges are cut only once by the tool, without repeated cutting. Existing optimization algorithms often prioritize material utilization, and only consider cutting cost and path optimization as auxiliary indicators. Therefore, the layout result is often difficult to balance between material utilization and cutting efficiency: the material utilization rate of the scheme is usually higher, and the cutting path is usually more complex, while the cutting efficiency of the scheme is usually higher, and there may be more waste. SUMMARY

[0004] The purpose of the present application is to provide a plate furniture part cutting layout and center tool path optimization method and system, which simultaneously considers material utilization and cutting path optimization during plate part layout, organically combines part layout and tool path planning, and realizes a reasonable balance between material utilization and cutting efficiency.

[0005] Technical scheme: The plate furniture part cutting layout and center tool path optimization method provided by the present application comprises the following steps:

[0006] Obtain the processing parameters;

[0007] Generate a candidate placement set for each part, and sort them in lexicographic order. Place all parts in order to obtain an initial layout;

[0008] In the lexicographic order, the first sorting key is the edge constraint cost function value, and the edge constraint cost function value CCF is:

[0009]

[0010] Wherein, S is the current candidate edge segment set, s is any candidate segment in the set S, is the length of the candidate segment ,​ a short edge count term, an indicator function that takes value 1 when the condition is met, and 0 otherwise, a short edge length threshold; a symmetry gap bias term, a candidate segment the actual gap between adjacent edges, a nominal gap between facing edges, i.e. the target gap; , an expected number of tabs from the segment length adaptive gap, a tab gap function that is adjusted adaptively by segment length: when > 600mm, when ≤ 600mm, , and are the target tab gaps for long and short segments, respectively; a total coincident edge length that can be formed by all candidate segments; an effective edge sticking length of a candidate segment to the inner board rectangular boundary; a weight coefficient greater than 0 for adjusting the influence weight of the short edge count term , the gap bias term , the expected number of tabs term , the coincident edge length term , and the edge sticking length term in the coincident edge constraint cost function CCF;

[0011] In the process of placing parts, the symmetry gap constraint, shared centerline extraction and tab de-duplication, and hierarchical knife lifting-free path generation are performed to obtain a final layout;

[0012] The processing file of the final layout of parts, shared centerline knife path, and tab arrangement is output.

[0013] Further, the processing parameters include: board boundary, part geometry, minimum safety gap, tool diameter , process tolerance , short edge length threshold .

[0014] Further, the dictionary sequence key further includes: the second sorting key is a geometric packing priority, the third sorting key is a combination index of board utilization rate improvement and excess material connectivity, and the fourth sorting key is a negative contact length.

[0015] Further, the execution of the symmetry gap constraint includes: in the process of placing parts, the symmetry gap is executed , allowing ; wherein is the nominal gap between the facing edges, i.e. the target gap, is the tool diameter, is the process tolerance.

[0016] Further, the shared centerline extraction and tab deduplication includes: identifying facing edges that satisfy , merging two outer contours into a single shared cutting centerline along the geometric centerline to replace double-edge cutting; and deduplicating and merging constraints on the repeated or dense tabs generated by sharing, limiting the number of tabs in each continuous tool path segment to no more than a preset number.

[0017] Further, the layered strategy is executed to organize the tool path, with internal feature layers being processed first, followed by the outer contour and shared centerline layers; and a continuous path is constructed in an end-to-end manner within each layer to reduce or avoid lifting the tool, and the segment sequence within the layer is locally reordered to shorten the empty travel.

[0018] Further, the placement of the part also includes a small plate constraint planning refinement: for sub-plates or local areas with an area or number of parts below a preset threshold, a constraint planning and / or integer programming model containing non-overlapping constraints and boundary constraints is established for fine tuning, so that the CCF is further reduced without violating the symmetric gap constraint, the shared centerline extraction and tab deduplication, and the layered tool path generation; and the refinement result is backfilled globally.

[0019] The plate furniture part cutting and center tool path optimization system provided by the application comprises:

[0020] A parameter acquisition unit is configured to acquire machining parameters.

[0021] A cutting and center tool path optimization unit is configured to generate a candidate placement set for each part, sort the candidate placement set according to a lexicographic key, sequentially place all the parts, and obtain an initial layout.

[0022] In the lexicographic key, a first sorting key is a common edge constraint cost function value, and the common edge constraint cost function value CCF is:

[0023]

[0024] wherein S is a current candidate common edge segment set, s is any candidate segment in the set S, is the length of the candidate segment , , is a short broken edge count term, is an indicator function that takes a value of 1 when the condition is met and a value of 0 otherwise, is a short edge length threshold value; is a symmetric gap deviation term, Candidate segment The actual seam distance between the seam and the adjacent side. The nominal spacing between opposing edges is the target gap. , The expected number of tabs is obtained by adaptive spacing based on segment length. For the tab spacing function that adaptively adjusts according to segment length, when When >600mm, ,when When ≤600mm, , and These represent the target tab spacing for the long and short segments, respectively. This is the sum of the common edge overlap lengths that can be formed by all candidate segments; The effective edge length between the candidate segment and the rectangular boundary of the inner plate; A weighting coefficient greater than 0 is used to adjust the short edge counting term separately. Gaps allowance deviation Tab number expected item , Length of Coincident Sides And edge length item Influence weights in the Common Edge Constraint Cost Function (CCF);

[0025] During the part placement process, symmetrical gap constraints, shared centerline extraction and Tab deduplication are performed, and layered tool-lift-free path generation is generated to obtain the final layout.

[0026] The output unit is used to output the final layout of the part coordinates, shared centerline toolpaths, and the machining file of the tab arrangement.

[0027] The computer-readable storage medium of the present invention stores a computer program, which, when executed by a processor, implements the method for cutting and layout of panel furniture parts and optimization of the center toolpath.

[0028] The computer program product of the present invention includes a computer program that, when executed by a processor, implements the panel furniture parts cutting and layout and center toolpath optimization method.

[0029] Beneficial effects: Compared with the prior art, the advantages of the present invention are as follows:

[0030] (1) Integrated optimization of layout and toolpath: In the candidate placement stage, the common edge constraint cost function (CCF) is introduced to unify and quantify the common edge / edge-attached reward, short broken edge and symmetrical gap deviation penalty, as well as Tab placement and path continuity. Combined with "fall-left push" compaction and dictionary order selection, the layout results have good processability and executability while ensuring material utilization.

[0031] (2) Cutting efficiency improvement: By using the edge-constrained centerline extraction and Tab adaptive (de-duplication), combined with the "nearest neighbor + 2-opt" segment sequence optimization and the strategies such as layered knife lifting-free, shallow cutting or crossing Tab in the last layer, the total length of air movement is controlled while reducing the number of down-cuts, shortening the estimated machine time, and reducing the number of tool start-stop times and tool wear. The effect is quantitatively compared in the embodiment. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 The method for cutting plate furniture parts of embodiment 1 is shown in the overall flow chart.

[0033] Figure 2 The candidate evaluation flow chart of the edge-constrained cost function of embodiment 1 is shown.

[0034] Figure 3 The reinforcement cycle flow chart of LNS and CP-SAT fine-tuning of embodiment 1 is shown.

[0035] Figure 4 The knife path generation and layered knife lifting-free strategy flow chart of embodiment 1 is shown.

[0036] Figure 5 The layout and knife path of a plate of embodiment 2 are shown in the schematic diagram.

[0037] Figure 6 The layout and knife path of another plate of embodiment 2 are shown in the schematic diagram.

[0038] Figure 7 The layout and knife path of another plate of embodiment 2 are shown in the schematic diagram.

[0039] Figure 8 The layout and knife path of another plate of embodiment 2 are shown in the schematic diagram. DETAILED DESCRIPTION

[0040] The technical solutions of the present application will be further described below in conjunction with the drawings. Unless otherwise stated, the terms "plate" and "single plate" refer to a piece of processed plate material.

[0041] Embodiment 1

[0042] As shown in the embodiment, the method generally includes the following steps in sequence: input and initialization, edge-constrained cost function and candidate evaluation, initial solution and multi-core multi-plate construction, LNS reinforcement and CP-SAT fine-tuning, and knife path generation and output statistics. Figure 1 S1, input and initialization.

[0043] S101, read and normalize parameters.

[0044] S101, read and normalize parameters.

[0045] Get rectangular parts list and process parameters. Parts are recorded as:

[0046] ;

[0047] where is the width (mm), is the number, indicates the rotatability. The size of the plate , the trimming of the four sides TRIM, the minimum allowance of the plate edge , the diameter D and radius of the tool . The "center distance = tool diameter" strategy is used for the common edge symmetry gap, and the nominal center distance of the opposite edges is set , then the adjacent part net gap is 0, and the common edge is completed by one tool.

[0048] S102, establish coordinates and net available area.

[0049] Establish a rectangular coordinate with the lower left corner of the plate as the origin, and the inner rectangular area that can be placed is , where:

[0050] ,

[0051] ,

[0052] .

[0053] To match the common edge center distance, use the placeholder rectangle when placing , and the actual equivalent center position of the part is .

[0054] S103, establish evaluation index.

[0055] Single board material utilization rate (according to the diameter of the placeable area):

[0056] where, and are the width and height of the th part, is the nominal distance of the opposite edges (target gap), and are the width and height of the net available rectangular area , and the sum is traversed for all placed parts on the current board .

[0057] Multi-board target priority: less board number → less remaining → common edge friendly.

[0058] S2, common edge constraint cost function establishment and candidate evaluation.

[0059] Referring to Figure 2 , the sequence relationship of candidate placement generation, edge abutment / collinear calculation, short edge and tab expectation evaluation, and CCF comprehensive scoring is shown, and S2 specifically includes the following steps.

[0060] S201, candidate generation.

[0061] For each part to be placed (including 0° / 90° two orientations), enumerate the placeable positions in the current free rectangle set , get the candidate .

[0062] S202, calculate the edge abutment / collinear reward.

[0063] Calculate the abutment length of the candidate with the four board edges ; the overlap length of the "potential collinear edge" formed with the placed parts (including horizontal and vertical directions, the symmetry relationship of the determination threshold ).

[0064] S203, calculate the short edge and tab expectation penalty.

[0065] If the visible segment length of the formed collinear edge or adjacent edge is less than the threshold (such as 120mm), the short edge penalty is counted; estimate the number of tabs expected according to the edge length (use the interval for long edges, and the interval for short edges):

[0066] .

[0067] S204, CCF definition (cost, the smaller the better).

[0068]

[0069] Where S is the current candidate collinear edge set, s is any candidate edge in the set S, is the length of the candidate edge , , is the short edge count item, is an indicator function that takes the value 1 when the condition is met, and 0 otherwise, is the short edge length threshold; , is the symmetric gap deviation item, is the actual gap between the candidate edge and the adjacent edge; . , is the expected number of tabs obtained by self-adaptive interval according to the edge length, Tab spacing function for segment length self-adaptive adjustment: when > 600 mm, when ≤ 600 mm, , and are the target tab spacing of long and short segments, respectively; is the total length of the common edge coincidence that can be formed by all candidate segments; is the effective edge length of the candidate segment and the inner plate rectangular boundary; is a weight coefficient greater than 0, used to adjust the influence weight of the short edge count term , the gap deviation term , the tab number expectation term , the common edge coincidence length term , and the edge length term in the common edge constraint cost function CCF;

[0070] S3, initial solution and multi-core multi-plate construction.

[0071] Referring to Figure 3 , S3 specifically includes the following steps.

[0072] S301, multi-core automatic single plate packing.

[0073] For the remaining piece set, the MaxRects algorithm, the Skyline algorithm, and the Beam algorithm are used in combination with the CCF function, respectively. The Beam node state score uses lexicographic order:

[0074] ;

[0075] That is, the highest processing line is given priority to the shorter one, the higher occupation is given priority to the higher one, and the longer formed common edge total length is given priority to the longer one ( is a small weight). The above three kinds of kernels can be used alone or mixed according to a preset proportion or an adaptive strategy, and the output single plate solution is uniformly entered into the subsequent steps.

[0076] S302, compaction and final plate annexation.

[0077] The "falling-left pushing" local geometric compaction operator is used for secondary compaction on each candidate plate solution output by step S301; if the final plate can be annexed to the previous plate as a whole, the final plate annexation operation is performed to reduce the number of plates. The multi-plate solution after compaction and final plate annexation is used as a candidate individual for GA / GRASP search.

[0078] S303, GA / GRASP driven multi-sequence search.

[0079] The group individual is coded as a gene string of "part sequence + kernel selection + beam width parameter". New sequences are generated by using the crossover and mutation operators of GA, and combined with the GRASP random greedy rearrangement strategy to generate new part sequences and kernel combinations. Steps S301 and S302 are called for each new individual to obtain the corresponding multi-board solution, and the evaluation and elite retention are performed according to the lexicographic order of "board number first, then smaller remaining area".

[0080] S4, LNS strengthening and CP-SAT fine-tuning.

[0081] Reference Figure 3 The iteration process of repeatedly improving the multi-board layout through large neighborhood destruction-repair and small board CP-SAT fine-tuning based on the multi-kernel construction initial solution is shown in the figure, and S4 includes the following steps.

[0082] S401, LNS destruction-repair cycle.

[0083] Randomly remove K parts from the current optimal multi-board solution, and perform "auto-pack backfill with CCF" for each board, and then try to swallow the last board, where K is a disturbance intensity parameter, which is a positive integer, preferably 1-3.

[0084] S402, CP-SAT small-scale fine-tuning.

[0085] When the number of single-board parts ( is the upper limit parameter of the number of parts triggering CP-SAT fine-tuning), construct a two-dimensional rectangular packing CP-SAT model for local fine-tuning of the current layout result of the board:

[0086] Variables: for each part on the board , define its lower left integer coordinates ;

[0087] Constraints: for the set of parts , call AddNoOverlap2D constraints to limit the non-overlapping of the occupation rectangles of each part , and impose boundary constraints , , ;

[0088] Objective ; where and are the width and height of the net available rectangular area of the single board, and are the occupation width and height of the part after considering the gap, Index set of parts to be refined on the board; the above target is used to press the whole part to the lower left corner area of the board to reduce the gap in a local range and make the layout close to the left lower boundary as much as possible while keeping the board size unchanged.

[0089] Solve timeout (such as 3s) to return the current optimal feasible solution.

[0090] S403, global search and simulated annealing acceptance criteria.

[0091] The overall goal is Evaluate the new solution, where, The number of plates used, The total remaining area (or equivalent remaining index) after multi-plate layout, The total length of the common edge and the edge contact that can be formed in each plate, The weight parameter that balances the impact of remaining and contact length; Respectively represent the objective function value of the current solution and the candidate new solution. If Unconditionally accept the new solution; otherwise, with a temperature Exponential probability Accept the new solution, and cool down according to ( , preferably ) until the time / convergence condition is reached.

[0092] S5, tool path generation and layering knife lifting free.

[0093] Referring to Figure 4 , the figure shows the complete tool path generation process of shared centerline extraction, Tab arrangement and deduplication, segment sorting and 2-opt refinement, and layering knife lifting free and G-code output, S5 includes the following steps.

[0094] S501, shared edge identification and centerline extraction.

[0095] In the finished layout, if the edges of two parts are parallel and the center distance is , it is determined as a shared edge, and the seam center is taken as the cutting centerline; single non-shared edge is offset by equal distance according to the tool radius r to form the centerline. Shared edges are extracted only once, naturally deduplicated.

[0096] S502, Tab automatic arrangement and deduplication.

[0097] Place the Tab at an adaptive interval according to the edge length, and deduplicate it with coordinate hashing. The Tab takes the last layer crossing (not cutting) as the default; if you need to keep the thickness shallow, you can set the cutting depth to .

[0098] S503, segment sorting and 2-opt refinement.

[0099] Using the bottom left corner of the available area as the starting point, an initial sequence is obtained using "nearest neighbor + direction selection", and equivalent costs are added between segments:

[0100]

[0101] in, This represents the Euclidean distance between the start and end points of the segment. and These are the weights for the empty-shift attack and Tab penalties, respectively. For indicator functions, This represents the number of tabs on this path.

[0102] The sequence is then subjected to a 2-opt swap to shorten the space shift.

[0103] S504, layered cutting and tool-free splicing.

[0104] Cutting is performed layer by layer according to the layer-by-layer cutting step distance: If the end point of the previous segment coincides with the start point of the next segment in a non-last layer, the cutting is continued directly without lifting the blade; when encountering a Tab segment in the last layer, the blade is lifted to cross it (or shallow cutting is performed according to the setting), thereby reducing the need for blade lifting and avoiding workpiece loosening.

[0105] S505, G-code output.

[0106] Generate a toolpath file containing spindle speed, feed, rapid descent, and layered G1 / G0 commands, and simultaneously export the layout PNG and toolpath PNG (including segment numbers and tab markers).

[0107] Example 2

[0108] This embodiment verifies the method described in this invention through specific experiments.

[0109] 1. Data and equipment parameters.

[0110] Dataset: 100 rectangular parts, 94 specifications (some specifications are repeated), with dimensions in millimeters. Sample data is shown in Table 1.

[0111] Original material: Length L=2440, Width W=1220, Thickness 18mm.

[0112] Tooling / Symmetrical Gap: Tool diameter D=6mm (symmetrical gap=6mm), symmetrical gap strategy, part center distance=tool diameter (one tool for common edge).

[0113] Process parameters: XY feed [3600mm / min], Z feed [600mm / min], spindle speed [18000rpm], safety height 5mm, layered tool entry distance 6mm.

[0114] Reserved section (Tab): long side interval 250mm, short side interval 150mm, reserved length 10mm, reserved thickness 1.2mm (final layer span or shallow cutting).

[0115] Table 1 Panel Data Table

[0116]

[0117] Appendix 1

[0118]

[0119] Appendix 2

[0120]

[0121] 2. The method of the application is used for cutting layout and center tool path optimization.

[0122] The method steps of this embodiment are the same as those of Example 1, and thus will not be described again. Some key steps are as follows:

[0123] 2.1, input and initialization.

[0124] Read the rectangular part list, the original plate and the tool / process parameters, establish a rectangular coordinate system at the lower left corner of the plate, and only allow the parts to rotate at 0° / 90°. According to the method of S101-S103 in Example 1, trim the plate and deduct the edge margin, determine the net available rectangular area, and introduce the placeholder rectangle and the calculation caliber of material utilization.

[0125] 2.2, single plate layout based on CCF.

[0126] In the current free rectangular set, enumerate all feasible placement positions that meet the non-overlapping constraint for each rectangular part to be placed (including 0° / 90° two orientations). The evaluation of the candidate position and the specific definition of the co-edge constraint cost function CCF are the same as the description of S201-S204 in Example 1, and will not be described again.

[0127] On this basis, the candidates are sorted according to the following lexicographic rules:

[0128] First, use CCF as the primary key to preferentially select candidates that are co-edge friendly, have less short scraps, and have reasonable Tab arrangement;

[0129] Second, compare the highest machining line height and the occupied area, and preferentially select the arrangement with lower highest machining line and larger occupied area;

[0130] Third, compare the total length of the formed co-edges, and preferentially select the state with longer co-edges.

[0131] ​The "falling-left push" compaction operation is performed on the best-ranked candidate until the current board can no longer accommodate new parts.

[0132] 2.3, Multi-board construction and reinforcement optimization.

[0133] First, use multi-core automatic packing strategies such as MaxRects, Skyline, and Beam to construct a single-board initial solution. The Beam state scoring rule is defined in step S301 of embodiment 1. Then, introduce a multi-board global optimization strategy of GA / GRASP, and update the part arrangement order and core parameters under the "priority of the number of boards, secondary remaining area, and edge friendliness" fitness function.

[0134] After obtaining the current better multi-board solution, further use large neighborhood search (LNS) to perform "move out-backfill" operation on a small number of parts to try to move across boards and annex the last board. For sub-boards with a small number of parts, call the NoOverlap2D constraint of CP-SAT for fine-tuning, and further improve the local layout quality while keeping the symmetry gap and edge geometry relationship from being destroyed.

[0135] 2.4, Tool path integration generation.

[0136] On the final layout, identify pairs of opposite edges that meet the actual center distance satisfy , and the opposite edges, and generate a shared cutting center line at the center of the gap; for single-piece outlines that do not meet the edge condition, offset by the tool radius r to get the entire set of cutting center lines.

[0137] According to the edge length, automatically arrange Tabs with adaptive spacing, and then use coordinate neighborhood judgment to remove and merge overlapping or too close Tabs to control the number of Tabs in each continuous tool path segment.

[0138] Take the lower left corner of the available area in the board as the starting point, use the "nearest starting point + penalty of lifting the tool / crossing the Tab" heuristic to construct the initial segment sequence, and then use 2-opt local exchange to refine the segment sequence and shorten the empty path. Finally, perform layering according to the layering strategy described in embodiment 1: try to maintain continuous lifting-free cutting within the layer in the non-final layer, and choose to lift or shallow cut when encountering a Tab segment in the final layer, to generate the corresponding layout PNG, tool path PNG, and G-code file that can be directly called by the machining center.

[0139] 3, Use existing conventional greedy layout and tool path generation methods to optimize the process of cutting layout and center tool path.

[0140] The comparison method does not introduce CCF, does not actively promote the common edge, and does not perform tool path layering and 2-opt optimization, represents a conventional greedy nesting and tool path generation scheme, and is used to compare the optimization effect of the method of the application.

[0141] 3.1, Nesting process.

[0142] Sort all rectangular parts according to the area from large to small.

[0143] From the first part after sorting, select the part in turn, and try to place it on the current board in the current orientation (0° / 90°).

[0144] On the current board, use the Bottom-Left or MaxRects rule: first, select the available rectangular area with the longest length in the vertical or horizontal direction, and then place the part in the area according to the principle of "left and bottom", without considering the common edge length and Tab arrangement, only requiring the fixed center distance D between parts to meet the basic safety distance of the tool diameter.

[0145] When there is no enough space on the current board to place the next part, end the board nesting, start a new board, and repeat the above steps until all parts are placed.

[0146] During the entire nesting process, no common edge recognition and common edge priority placement are performed, and no common edge constraint cost function (CCF) is used to reorder the candidate positions.

[0147] 3.2, Tool path generation process.

[0148] On the basis of the nesting result, a closed cutting path is generated for the outer contour of each part, and each part is regarded as an independent cutting segment.

[0149] All part cutting segments use a simple "nearest neighbor" strategy for concatenation: starting with a fixed starting point, each time the nearest unprocessed part contour to the current end point is selected as the next segment, and no 2-opt or other local optimization is performed on the segment sequence.

[0150] Tabs are arranged at several equidistant positions on each part contour, with a fixed tab spacing that is not self-adaptive according to the edge length, and repeated tabs caused by common edges or path overlap are not removed or merged.

[0151] During the tool path generation process, no layering lift strategy is used, and after each part contour cutting is completed, the tool is lifted and moved quickly, and then the tool is cut for the next part, resulting in a large number of lift times and empty movement distances.

[0152] 4, the method described in the application is used for cutting nesting and center tool path optimization results.

[0153] Overall index: number of plates 13; plate material utilization ; total length of common edge sharing cut ; ; total empty movement distance ; estimated processing time . Among them, represents the plate material utilization, represents the total length of the shared cutting center line formed by the common edge and the edge, represents the number of knife lifting times in the center knife path, represents the total length of the tool empty movement path, represents the processing time estimated based on the length of the knife path and the feed speed.

[0154] In order to intuitively show the layout and knife path effect of the method described in the application, four representative plate layout and knife path diagrams are selected in this embodiment.

[0155] As shown in Figure 5 , it is a layout and knife path diagram of the first plate in Example 2. A large number of medium and large size rectangular parts on the plate surface are aligned in the horizontal and vertical directions, forming a plurality of long common edge chains penetrating the plate surface. The cutting center line is arranged continuously along the common edge, and only one cut is needed for the shared center line to complete the processing of the parts on both sides; the knife path in the same layer is continuous as a whole, and only a small amount of empty movement occurs when switching between layers or locally crossing. Tabs are evenly arranged along the plate contour and main common edge segment, which ensures the fixation of the parts without significantly damaging the continuity of the tool path.

[0156] As shown in Figure 6 , it is a layout and knife path diagram of a plate with relatively dense common edges. A plurality of narrow and long rectangular parts are aligned in the height direction, forming a plurality of longitudinal long common edge chains along the plate height direction. The knife path is sequentially cut along these longitudinal common edge chains, forming a plurality of long distance lift-free paths in the same layer; only short distance empty movement occurs when jumping or crossing individual tabs between different common edge chains. It can be seen that the common edge constraint cost function indeed promotes the formation of long common edge chains and improves the continuity of the tool path during the layout stage.

[0157] As shown in Figure 7 , it is a typical plate layout and knife path diagram after GA / GRASP multi-plate optimization and LNS swallowing the last plate. Large size rectangular parts are distributed at the top of the plate, and a plurality of medium and small size parts are embedded at the bottom, and the remaining blank is concentrated on one side of the plate, and the overall layout is compact. Some parts originally located in the last plate are moved to this plate, reducing the number of plates used. The common edge chains are continuously distributed between the large and small parts, and the knife path sequentially connects between the long common edge and the outer contour, and the segment empty movement path is relatively short, which is consistent with the statistical results of .

[0158] As Figure 8 shown, it is a schematic diagram of layout and tool path of a plate with more long strip parts. A plurality of long strip rectangular parts are arranged in the middle and lower part of the plate, and are continuously aligned in the horizontal direction to form one or more horizontal long common edge chains. A large number of tabs are arranged along the long common edge and the outer contour edge at an adaptive spacing according to the edge length, and the number of single tabs is controlled by de-duplication and merging to ensure the rigidity and stability of the long strip parts in the cutting process. The tool path continuously cuts along the long common edge and the outer contour in the same layer, and the lifting action mainly occurs at the position of layer switching or across the tab, which directly shows the common edge center line extraction, tab adaptive arrangement and layering lift-free effect of the invention in the long strip part scene.

[0159] 5、Two methods of comparison.

[0160] Table 2 shows the comparison of the results of the conventional greedy method without common edge constraint function and the method of the invention in the optimization of cutting layout and center tool path under the same large plate specification and tool / feed parameters. From the table, compared with the conventional greedy method without considering the common edge constraint function, the number of plates is reduced from 14 to 13, the material utilization rate is improved from 82.86% to 89.23%, and the total length of the common edge is obtained. 60000.7mm, the number of lifting is reduced from 1500 to 1354, the total length of empty movement is reduced from 261849.4mm to 233338.6mm, and the machine time is shortened from 399.97min to 315.5min. It can be seen that the method of the invention can significantly improve the plate utilization rate and shorten the processing time while reducing the number of plates, and the overall layout and tool path is more reasonable and efficient.

[0161] Table 2 Comparison of experimental results of no common edge constraint function method and the invention

[0162]

[0163] Example 3

[0164] The plate furniture part cutting layout and center tool path optimization system provided by the invention comprises:

[0165] The parameter acquisition unit is used to acquire the processing parameters.

[0166] The cutting layout and center tool path optimization unit is used to generate a candidate placement set for each part, sort the candidate placement set according to the lexicographic key, and sequentially place all parts to obtain an initial layout.

[0167] In the lexicographic key, the first sorting key is the common edge constraint cost function value, and the common edge constraint cost function value CCF is:

[0168]

[0169] where S is the current candidate co-edge segment set, s is any candidate segment in set S, is the length of candidate segment s, , is the short broken edge count term, is the indicator function that takes value 1 when the condition is true, otherwise 0, is the short edge length threshold; is the symmetric gap bias term, is the actual gap distance between candidate segment s and adjacent edge, ; , , is the expected Tab number from the segment length adaptive gap, is the Tab gap function that is adaptively adjusted by segment length: when > 600mm, when ≤ 600mm, , and are the target Tab gap for long and short segments respectively; is the total co-edge overlap length that can be formed by all candidate segments; is the effective tabbing length of candidate segment s to the inner plate rectangular boundary; is the weight coefficient greater than 0 for adjusting the influence weight of short broken edge count term , gap bias term , Tab number expectation term , co-edge overlap length term and tabbing length term in the co-edge constraint cost function CCF;

[0170] In the process of placing parts, symmetric gap constraint, shared centerline extraction and Tab deduplication and hierarchical knife lifting-free path generation are performed to obtain the final layout;

[0171] An output unit is configured to output the part coordinates of the final layout, the shared centerline tool path and the Tab arrangement processing file.

[0172] Embodiment 4

[0173] The computer readable storage medium described in the application stores a computer program, and the computer program is executed by a processor to realize the plate furniture part cutting layout and center tool path optimization method.

[0174] ​The computer readable storage medium can include a RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer.

[0175] The processor is configured to execute the computer program stored in the memory to implement each step in the method involved in the above-mentioned embodiments.

[0176] Embodiment 5

[0177] The computer program product described in the present application comprises a computer program, which, when executed by a processor, implements the plate furniture part cutting and layout optimization method according to the description.

Claims

1. A method for panel furniture parts cutting layout and center toolpath optimization, characterized in that, Includes the following steps: Obtain processing parameters; For each part, generate a set of candidate placement positions, sort them according to the dictionary key, and place all parts in order to obtain the initial layout; In the dictionary order key, the first sort key is the common edge constraint cost function value (CCF), which is calculated using the following formula: ； Where S is the current set of candidate edge-sharing segments, and s is any candidate segment in set S. Candidate segment Length, , For the short fragmented edge counting term, An indicator function that takes the value 1 when the condition is true and 0 otherwise. The threshold for the short side length; , is the symmetrical gap deviation term. Candidate segment The actual seam distance between the seam and the adjacent side. The nominal spacing between opposing edges is the target gap. , The expected number of tabs is obtained by adaptive spacing based on segment length. The tab spacing function is adaptively adjusted according to segment length: when When >600mm, ,when When ≤600mm, , and These represent the target tab spacing for the long and short segments, respectively. This is the sum of the common edge overlap lengths that can be formed by all candidate segments; The effective edge length between the candidate segment and the rectangular boundary of the inner plate; A weighting coefficient greater than 0 is used to adjust the short edge counting term separately. Gaps allowance deviation Tab number expected item , Length of Coincident Sides and edge length item Influence weights in the Common Edge Constraint Cost Function (CCF); During the part placement process, symmetrical gap constraints, shared centerline extraction and Tab deduplication are performed, and layered tool-lift-free path generation is generated to obtain the final layout. Output the final layout of the part coordinates, shared centerline toolpaths, and the machining file with tab arrangement.

2. The method for panel furniture parts cutting layout and center toolpath optimization according to claim 1, characterized in that, The processing parameters include: sheet metal boundary, part geometry, minimum safety clearance, and tool diameter. Process tolerance Short side length threshold .

3. The method for panel furniture parts cutting layout and center toolpath optimization according to claim 1, characterized in that, The dictionary sequence key also includes: The second sorting key is the geometric filling priority, the third sorting key is the combined index of plate utilization improvement and residual material connectivity, and the fourth sorting key is the negative contact length.

4. The method for panel furniture parts cutting layout and center toolpath optimization according to claim 1, characterized in that, The execution of the symmetrical gap constraint includes: Execution during part placement Symmetrical gaps allow ;in The nominal spacing between opposing edges is the target gap. The diameter of the cutting tool. This is for process tolerance.

5. The method for panel furniture parts cutting layout and center toolpath optimization according to claim 4, characterized in that, Performing the shared centerline extraction and Tab deduplication includes: Identify and satisfy The opposing edges merge the two outer contours along the geometric center line into a single shared cutting center line to replace the double-sided cutting; the duplicate or overly dense Tabs generated by sharing are deduplicated and merged, limiting each continuous cut segment to no more than a preset number of Tabs.

6. The method for panel furniture parts cutting layout and center toolpath optimization according to claim 1, characterized in that, The generation of the layered tool-free path includes: The toolpath is organized using a layered strategy, prioritizing the machining of internal feature layers, followed by machining of outer contour layers and shared centerline layers. Within each layer, continuous paths are constructed by connecting endpoints to reduce or avoid tool lifting, and the segment sequence within each layer is locally reordered to shorten the idle travel.

7. The method for panel furniture parts cutting layout and center toolpath optimization according to claim 1, characterized in that, The process of placing parts also includes fine-tuning of small plate constraint planning: for sub-plates or local areas with an area or number of parts below a preset threshold, a constraint planning and / or integer planning model containing non-overlapping constraints and boundary constraints is established for fine-tuning and solving, so that the CCF is further reduced without destroying the symmetric gap constraint, shared centerline extraction and Tab deduplication, and layered tool-free path generation; the fine-tuning results are then backfilled into the global system.

8. A panel furniture parts cutting and layout and center toolpath optimization system, characterized in that, include: The parameter acquisition unit is used to acquire processing parameters; The cutting and layout and center toolpath optimization unit is used to generate a set of candidate placement positions for each part, sort them according to the dictionary key, and place all parts in sequence to obtain the initial layout; In the dictionary order key, the first sort key is the common edge constraint cost function value (CCF), which is: ； Where S is the current set of candidate edge-sharing segments, and s is any candidate segment in set S. Candidate segment Length, , For the short fragmented edge counting term, An indicator function that takes the value 1 when the condition is true and 0 otherwise. The threshold for the short side length; , is the symmetrical gap deviation term. Candidate segment The actual seam distance between the seam and the adjacent side. The nominal spacing between opposing edges is the target gap. , The expected number of tabs is obtained by adaptive spacing based on segment length. The tab spacing function is adaptively adjusted according to segment length: when When >600mm, ,when When ≤600mm, , and These represent the target tab spacing for the long and short segments, respectively. This is the sum of the common edge overlap lengths that can be formed by all candidate segments; The effective edge length between the candidate segment and the rectangular boundary of the inner plate; A weighting coefficient greater than 0 is used to adjust the short edge counting term separately. Gaps allowance deviation Tab number expected item , Length of Coincident Sides and edge length item Influence weights in the Common Edge Constraint Cost Function (CCF); During the part placement process, symmetrical gap constraints, shared centerline extraction and Tab deduplication are performed, and layered tool-lift-free path generation is generated to obtain the final layout. The output unit is used to output the final layout of the part coordinates, shared centerline toolpaths, and the machining file of the tab arrangement.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the panel furniture parts cutting and layout and center toolpath optimization method according to any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the panel furniture parts cutting and layout and center toolpath optimization method according to any one of claims 1-7.

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