Cutting optimization method and system for EVA lining for tool box

Abstract

The invention discloses a cutting optimization method and system for an EVA lining for a tool box, and relates to the technical field of cutting optimization, and the method comprises the steps: carrying out the reverse size compensation of the tool box lining based on the nonlinear shrinkage of a thermal cutting material, and obtaining a pre-expansion compensation model; constructing a dynamic nested boundary according to the raw material design information, and performing periodic nested splicing fitting of the pre-expansion compensation model to obtain a plurality of candidate cyclic splicing structures; based on the curve path planning, fitting and outputting a plurality of candidate cutting paths of the plurality of candidate cyclic splicing structures; calculating a plurality of material utilization rates of the plurality of candidate cyclic splicing structures; and carrying out cutting characteristic analysis according to the plurality of material utilization rates and the plurality of candidate cutting paths, screening out a target nested segmentation structure, and executing cyclic segmentation of the EVA lining for the tool box. The technical problems of extensive typesetting layout and low material utilization rate in the prior art are solved, and the technical effects of improving the material utilization rate and reducing the waste rate are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of cutting optimization, and particularly relates to a cutting optimization method and system for an EVA lining of a tool box. Background Art

[0002] In the manufacturing process of the EVA lining of a tool box, the EVA board is usually cut graphically to achieve customized storage. However, in the existing processing flow, such as an EVA material cutting device provided by CN219114126U, the layout mostly depends on manual operation or simple two-dimensional nesting software for preliminary arrangement, without deeply optimizing by combining the geometric characteristics of the lining structure and the actual size of the raw material board, resulting in a relatively rough arrangement of the lining graphics on the board, unable to form an efficient space nesting mode, generating a large amount of unusable corner scraps, and causing low material utilization rate. Summary of the Invention

[0003] This application provides a cutting optimization method and system for an EVA lining of a tool box, which is used to solve the technical problems of rough layout and low material utilization rate in the prior art.

[0004] In view of the above problems, this application provides a cutting optimization method and system for an EVA lining of a tool box.

[0005] In the first aspect of this application, a cutting optimization method for an EVA lining of a tool box is provided. The method includes: Performing reverse dimensional compensation on the tool box lining based on the non-linear shrinkage of the thermal cutting material to obtain a pre-expansion compensation model; interactively obtaining the raw material design information of the raw material board, and constructing a dynamic nesting boundary according to the raw material design information; performing periodic nesting splicing fitting of the pre-expansion compensation model within the dynamic nesting boundary to obtain a plurality of candidate cyclic splicing structures; fitting and outputting a plurality of candidate cutting paths of the plurality of candidate cyclic splicing structures based on curve path planning; calculating the material utilization rates of the plurality of candidate cyclic splicing structures according to the dynamic nesting boundary and the plurality of candidate cutting paths; performing cutting characteristic analysis according to the plurality of material utilization rates and the plurality of candidate cutting paths to screen and select a target nesting segmentation structure from the plurality of candidate cyclic splicing structures; and performing cyclic segmentation of the EVA lining of the tool box using the target nesting segmentation structure.

[0006] In the second aspect of this application, a cutting optimization system for an EVA lining of a tool box is provided. The system includes: Reverse dimension compensation module, used to perform reverse dimension compensation on the inner lining of the tool box based on the non-linear shrinkage of the thermally cut material to obtain a pre-expansion compensation model; nested boundary construction module, used to interactively obtain the raw material design information of the raw material sheet and construct a dynamic nested boundary according to the raw material design information; splicing and fitting module, used to perform periodic nested splicing and fitting of the pre-expansion compensation model within the dynamic nested boundary to obtain a plurality of candidate cyclic splicing structures; cutting path determination module, used to fit and output a plurality of candidate cutting paths of the plurality of candidate cyclic splicing structures based on curve path planning; utilization rate calculation module, used to calculate the material utilization rates of the plurality of candidate cyclic splicing structures according to the dynamic nested boundary and the plurality of candidate cutting paths; cutting characteristic analysis module, used to perform cutting characteristic analysis according to the plurality of material utilization rates and the plurality of candidate cutting paths to screen out the target nested segmentation structure from the plurality of candidate cyclic splicing structures by decision-making; cyclic segmentation module, used to perform cyclic segmentation of the EVA inner lining for the tool box by using the target nested segmentation structure.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application performs reverse dimension compensation on the inner lining of the tool box based on the non-linear shrinkage of the thermally cut material to obtain a pre-expansion compensation model; interactively obtains the raw material design information of the raw material sheet and constructs a dynamic nested boundary according to the raw material design information; performs periodic nested splicing and fitting of the pre-expansion compensation model within the dynamic nested boundary to obtain a plurality of candidate cyclic splicing structures; fits and outputs a plurality of candidate cutting paths of the plurality of candidate cyclic splicing structures based on curve path planning; calculates the material utilization rates of the plurality of candidate cyclic splicing structures according to the dynamic nested boundary and the plurality of candidate cutting paths; performs cutting characteristic analysis according to the plurality of material utilization rates and the plurality of candidate cutting paths to screen out the target nested segmentation structure from the plurality of candidate cyclic splicing structures by decision-making; performs cyclic segmentation of the EVA inner lining for the tool box by using the target nested segmentation structure. The present invention solves the technical problems of rough layout and low material utilization rate in the prior art. By importing the inner lining shape data and raw material size data, combining with an optimization algorithm for periodic nested arrangement and cutting path fitting, and dynamically adjusting the cutting process parameters, the technical effects of improving the material utilization rate and reducing the waste rate are achieved. Description of the Drawings

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0009] Figure 1 Schematic flowchart of a cutting optimization method for an EVA lining of a tool box provided by an embodiment of the present application; Figure 2 Schematic structural diagram of a cutting optimization system for an EVA lining of a tool box provided by an embodiment of the present application.

[0010] Explanation of reference numerals: reverse dimension compensation module 11, nested boundary construction module 12, splicing fitting module 13, cutting path determination module 14, utilization rate calculation module 15, cutting characteristic analysis module 16, loop division module 17. Detailed implementation manners

[0011] The present application provides a cutting optimization method and system for an EVA lining of a tool box. To solve the technical problems of rough layout and low material utilization rate in the prior art, by importing the lining shape data and raw material size data, combining with an optimization algorithm for periodic nested arrangement and cutting path fitting, and dynamically adjusting the cutting process parameters, the technical effects of improving the material utilization rate and reducing the scrap rate are achieved.

[0012] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0013] It should be noted that any variations of the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server including a series of steps or units is not necessarily limited to those clearly listed steps or units, but may include other steps or modules not clearly listed or inherent to these processes, methods, products or devices.

[0014] Embodiment 1, as Figure 1 shown, the present application provides a cutting optimization method for an EVA lining of a tool box, and the method includes: Step S100: Perform reverse dimension compensation on the tool box lining based on the non-linear shrinkage of the thermal cutting material to obtain a pre-expansion compensation model.

[0015] In the embodiment of the present application, by constructing a lining design model of the tool box EVA lining, combining the local cutting temperature field distribution and the thermal expansion coefficient of the EVA material, a non-linear shrinkage model for depicting the size change law during the thermal cutting process is established, and reverse dimension compensation is performed on the lining design model based on this model, thereby generating a pre-expansion compensation model with the ability to correct thermal shrinkage errors.

[0016] Further, in the method provided by the application embodiment, based on the non-linear shrinkage of the thermally cut material, reverse dimensional compensation is performed on the inner lining of the tool box to obtain a pre-expansion compensation model, and it further includes: According to the inner lining structure design parameters of the EVA inner lining of the tool box, an inner lining design model is constructed; the cutting temperature field distribution is locally called, and a non-linear shrinkage model is established according to the thermal expansion coefficient of the EVA material to be cut and the cutting temperature field distribution; the non-linear shrinkage model is used to perform reverse dimensional compensation on the inner lining design model to generate the pre-expansion compensation model.

[0017] In the embodiment of the present application, first, according to the product design drawings of the EVA inner lining, the inner lining structure design parameters including the boundary contour, groove structure, slot position and depth, etc. are extracted, and a digital inner lining design model is established using a CAD modeling tool (such as SolidWorks or AutoCAD).

[0018] Then the cutting temperature field distribution is locally called. To obtain the temperature distribution of the EVA material during thermal cutting, the finite element heat conduction simulation method is used. Through a thermal simulation platform such as ANSYS, the cutting path data and process parameters (such as cutting speed, heat source diameter, power density) are imported, and a two-dimensional planar thermal field model of the EVA plate is established. During the simulation process, the material thermal physical properties parameters (thermal conductivity, specific heat capacity, density) and the convective and radiative boundary conditions are set, and the time step simulation is performed to output the temperature change results of the surface and inside of the EVA plate at each moment under the movement of the heat source along the cutting trajectory. The finally obtained cutting temperature data is based on the spatial coordinates and is output as a thermal distribution array with a time dimension, that is, the cutting temperature field distribution.

[0019] Then a non-linear shrinkage model is established according to the thermal expansion coefficient of the EVA material to be cut and the cutting temperature field distribution. In this step, first, by referring to the EVA material data manual, its thermal expansion coefficient α (for example, 2×10⁻ 4( / ℃), which represents the expansion ratio per unit length for every 1℃ increase in temperature. Then, using the point-by-point temperature mapping method, the temperature T(x, y) at each coordinate point (x, y) in the cutting temperature field distribution is extracted, and the linear contraction ratio at this position is calculated through the formula ε(x, y)=α×T(x, y). Next, using the component vector projection method, the contraction ratio at each position is decomposed along the x-axis and y-axis directions. The specific method is to set the angle θ(x, y) between the normal direction of each point and the x-axis, and calculate the contraction offsets in the x-direction and y-direction according to trigonometric functions, which are Δx(x, y)=ε(x, y)×L0×cosθ(x, y) and Δy(x, y)=ε(x, y)×L0×sinθ(x, y) respectively, where L0 is the reference unit length. By forming vectors from Δx and Δy and constructing a complete two-dimensional contraction vector set according to the coordinate system of the original design model, a non-linear contraction model is obtained.

[0020] Finally, the non-linear contraction model is used to perform reverse dimension compensation on the lining design model. Using the coordinate offset method, all the contour points affected by heat in the lining design model are translated backward according to the offset vector given by the non-linear contraction model. For example, if a point is expected to contract by 0.5mm, then this point is translated outward by 0.5mm during modeling. The contour is geometrically expanded through the point-by-point correction method to complete the compensation operation and generate the final pre-expansion compensation model.

[0021] Step S200: Interactively obtain the raw material design information of the raw material sheet and construct a dynamic nested boundary according to the raw material design information.

[0022] In the embodiment of the present application, first, the raw material design information of the raw material sheet is interactively obtained. The operator inputs the basic parameters of the raw material sheet through the human-machine interaction interface, including length, width, thickness, edge treatment method, etc. At the same time, historical warehousing data can be automatically obtained by docking with the ERP or MES system, or standard sheet information can be quickly imported through barcode scanning to form standardized raw material design information.

[0023] Subsequently, the edge tolerance correlation index is locally called, where the edge tolerance correlation index includes cutting equipment accuracy and clamping mechanism error; after constructing the raw material design model according to the raw material design information, a rectangular effective processing area is delimited from the raw material design model based on the edge tolerance correlation index. Next, the cutting edge boundary characteristics are interactively obtained, and a thermal deformation buffer zone is constructed according to the cutting edge boundary characteristics; finally, the thermal deformation buffer zone is superimposed on the rectangular effective processing area to obtain a dynamic nested boundary.

[0024] Further, in the method provided by the embodiment of the application, interactively obtaining the raw material design information of the raw material sheet and constructing a dynamic nested boundary according to the raw material design information further includes: Local call of edge tolerance correlation indicators, where the edge tolerance correlation indicators include cutting equipment accuracy and clamping mechanism error; after constructing a raw material design model based on the raw material design information, a rectangular effective processing area is delimited from the raw material design model according to the edge tolerance correlation indicators; the cutting edge boundary features are obtained interactively, and a thermal deformation buffer zone is constructed according to the cutting edge boundary features; the thermal deformation buffer zone is superimposed on the rectangular effective processing area to obtain the dynamic nested boundary.

[0025] In the embodiment of the present application, first, the edge tolerance correlation indicators are locally called. Using the parameter table reading method, by calling the preset equipment error parameter table in the system database, the processing accuracy of the current cutting equipment (such as positioning accuracy ±0.5 mm) and the clamping mechanism error (such as the clamp blocking edge by 8 mm) are obtained. These indicators are measured during equipment debugging or provided by the manufacturer, saved in the form of a standard table, and called through the rule engine.

[0026] Then, a raw material design model is constructed according to the raw material design information. This modeling step uses the CAD geometric modeling method. Key parameters such as the length, width, and thickness of the sheet are obtained through the human-computer interaction interface, or the raw material information table is called through the ERP / MES system to generate the standard sheet specifications, and a two-dimensional rectangular boundary is drawn in the CAD modeling software (such as AutoCAD or SolidWorks) to form the raw material design model.

[0027] After the modeling is completed, a rectangular effective processing area is delimited from the raw material design model according to the edge tolerance correlation indicators. This delimitation process uses the boundary inward offset method. Specifically, each boundary line representing the edge of the sheet in the raw material design model calculates the minimum safety margin according to the called cutting equipment accuracy and clamping mechanism error. For example, when the cutting accuracy is ±0.5 mm and the clamping error is 8 mm, it automatically offsets inward by ≥8.5 mm, eliminates the high-error-risk areas, and retains the controllable area in the middle, finally obtaining the rectangular effective processing area.

[0028] Subsequently, the cutting edge boundary features are obtained interactively through visual inspection or manual input. The specific methods include using a line array CCD camera to scan the edge of the sheet to identify defect areas such as cracks, warping, and burning, or manual marking of the boundary defects by the operator in the visualization interface. The above features are recorded in the form of coordinate points or bounding boxes and are used as spatial interference sources for boundary safety correction.

[0029] Based on the feature recognition, a thermal deformation buffer zone is constructed according to the cutting edge boundary features. The construction method uses the fixed expansion radius method. A geometric expansion operation is performed on each identified defect area, such as forming a rounded closed polygon within 10 mm around the defect, to construct a thermal deformation buffer zone with thermal deformation isolation function, which is used to eliminate the thermally unstable areas during layout.

[0030] Finally, the thermal deformation buffer zone is superimposed on the rectangular effective processing area. The superimposition method uses Boolean set operations (such as difference set operation) to exclude the thermal deformation buffer zone from the rectangular effective processing area, obtaining the arrangeable space area after comprehensively considering the equipment accuracy, clamping interference, and edge thermal deformation effects, and finally outputting it as a dynamic nested boundary.

[0031] Furthermore, the method provided by the application embodiment further includes: Using a linear array CCD camera to scan the surface of the raw material plate to locate the distribution of multiple surface defects; according to the size characteristics of the distribution of the multiple surface defects, matching circular disabled grids; using the circular disabled grids to perform center coverage with the centers of the multiple surface defect distributions, and globally calibrating the disabled grids within the dynamic nested boundary.

[0032] In the embodiment of the present application, first, a linear array CCD camera is used to scan the surface of the raw material plate. This step uses a linear array CCD camera set under an online scanning trajectory, in cooperation with a moving platform or following the motion path of the cutting machine synchronously, to collect high-resolution images of the entire plate surface. After the image collection is completed, the abnormal areas in the image are analyzed through image processing methods (such as edge detection, morphological analysis, and connected component labeling) to locate the distribution of multiple surface defects, including local defects such as indentations, bubbles, pits, and scratches. This defect information is output in the form of a two-dimensional coordinate point set, and each defect point corresponds to a set of position information and area size.

[0033] Next, according to the size characteristics of the distribution of multiple surface defects, circular disabled grids are matched. To facilitate subsequent rasterization arrangement calculations, multiple standard circular grid units are preset in advance (for example, three specifications with diameters of 5mm, 10mm, and 15mm), and by comparing the maximum circumscribed diameter of each surface defect distribution, the smallest circular disabled grid that is sufficient to completely cover the defect area is selected as the occlusion unit for this defect.

[0034] Then, using the circular disabled grids to perform center coverage with the centers of the multiple surface defect distributions, that is, in the two-dimensional coordinate system of the raw material plate, with the center point of each defect as the center, draw circular disabled grids with corresponding diameters, cover them on the plate surface layer, and form a geometric occlusion relationship with the arranged graphics. Finally, all the disabled grids generated in this operation are summarized, and the disabled grids are globally calibrated within the dynamic nested boundary. The calibration process records the position, coverage radius, center coordinates, and number of each circular disabled grid by traversing the two-dimensional coordinate system of the entire dynamic nested boundary, and forms a unified spatial grid disabled mapping table. This mapping result serves as the input hard constraint for the nested optimization algorithm and the path planning engine to complete the global calibration of the disabled grids.

[0035] Step S300: Perform periodic nested stitching fitting of the pre-inflation compensation model within the dynamic nested boundary to obtain multiple candidate cyclic stitching structures.

[0036] In an embodiment of the present application, when performing periodic nested stitching fitting of the pre-inflation compensation model within the dynamic nested boundary, first extract the basic geometric unit and the transitional connection structure through topological analysis; subsequently, pre-define a nested repetition rule composed of a translational repetition limit and a rotational repetition limit; under the constraint of this rule, first perform equidistant replication of the basic geometric unit in the biaxial direction to generate M translational cyclic stitching structures; then, based on the rotational repetition limit, perform rotational replication of the transitional connection structure within the misaligned area to form M groups of rotational cyclic stitching structures; finally, map and combine the translational and rotational structures to output multiple candidate cyclic stitching structures.

[0037] Further, in the method provided by the embodiment of the application, performing periodic nested stitching fitting of the pre-inflation compensation model within the dynamic nested boundary to obtain multiple candidate cyclic stitching structures further includes: Perform topological analysis on the pre-inflation compensation model to extract the basic geometric unit and the transitional connection structure; pre-define a nested repetition rule, where the nested repetition rule is composed of a translational repetition limit and a rotational repetition limit; according to the translational repetition limit, perform two-way equidistant replication of the basic geometric unit along the Y-axis direction and the X-axis direction within the dynamic nested boundary until nested cycling, and output M translational cyclic stitching structures; within the M misaligned spatial distributions of the dynamic nested boundary and the M translational cyclic stitching structures, perform misaligned replication of the transitional connection structure based on the rotational repetition limit until nested cycling, and output M groups of rotational cyclic stitching structures; map and combine the M translational cyclic stitching structures and the M groups of rotational cyclic stitching structures to obtain the multiple candidate cyclic stitching structures.

[0038] In an embodiment of the present application, first perform topological analysis on the pre-inflation compensation model to extract the basic geometric unit and the transitional connection structure. This step uses the boundary contour decomposition method. By calling the boundary recognition tool in the CAD modeling software, the two-dimensional contour of the pre-inflation compensation model is split, and the main graphic with closed and repeatable features is recognized as the basic geometric unit, and the auxiliary bridging structure connecting the two units is recognized as the transitional connection structure.

[0039] Subsequently, pre-define a nested repetition rule, where the nested repetition rule is composed of a translational repetition limit and a rotational repetition limit. This process uses the parameter setting method, and the user sets the control parameters of the nested replication behavior in the interface. The translational repetition limit ensures that there is no overlap between units by setting the minimum repetition spacing in the X-axis and Y-axis directions; the rotational repetition limit controls the rotational adaptation mode of the transitional connection structure in the boundary gap by limiting the allowed rotation angles (such as 90°, 180°).

[0040] Then, according to the translation repetition limit, perform two-way equidistant replication of the basic geometric unit along the Y-axis and X-axis directions on the dynamic nested boundary until the nested loop, and output M translation loop splicing structures. This operation uses the equidistant array replication method, calls the two-dimensional graphic replication command in the layout module, arranges the basic geometric unit along the X-Y axis directions based on the set pitch until the dynamic nested boundary is filled, automatically identifies the replication end point, and outputs M translation loop splicing structures with unified directions and compact structures.

[0041] On this basis, to fill the local misalignment gaps existing between the M translation loop splicing structures, perform misalignment replication of the transition connection structure based on the rotation repetition limit in the M misalignment spaces distributed between the dynamic nested boundary and the M translation loop splicing structures until the nested loop. This process uses the angle rotation matching method, rotates the transition connection structure by a defined angle (such as ±90°) according to the rotation repetition limit, and attempts to embed it into the corner or diagonal areas not covered by the translation structure until all the adapted positions in the dynamic nested boundary are filled, and output M groups of rotation loop splicing structures.

[0042] Finally, map and combine the M translation loop splicing structures and the M groups of rotation loop splicing structures. In this process, restore the basic geometric unit and the transition connection structure to the standard structure proportion characteristics used for calculation in the pre-expansion compensation model as the nested structure evaluation benchmark; after mapping and combining the M translation loop splicing structures and the M groups of rotation loop splicing structures, calculate the real-time structure proportion characteristics of each group of combined structures; then compare the real-time structure proportion characteristics of each group with the standard structure proportion characteristics, screen out the structure combinations that meet the set proportion accuracy requirements, and finally obtain multiple candidate loop splicing structures.

[0043] Further, in the method provided by the application embodiment, mapping and combining the M translation loop splicing structures and the M groups of rotation loop splicing structures to obtain the multiple candidate loop splicing structures further includes: Restore the basic geometric unit and the transition connection structure to the standard structure proportion characteristics calculated by the pre-expansion compensation model; after mapping and combining the M translation loop splicing structures and the M groups of rotation loop splicing structures, calculate and output the real-time structure proportion characteristics of M groups; screen to obtain the multiple candidate loop splicing structures according to whether the real-time structure proportion characteristics of the M groups meet the standard structure proportion characteristics, where each candidate loop splicing structure is composed of a translation loop splicing structure and a rotation loop splicing structure.

[0044] In the embodiments of the present application, first, the basic geometric units and the transition connection structures are restored to the standard structural proportion characteristics of the pre-expansion compensation model. This step adopts the graphic configuration restoration method. In the modeling software, the initial design state of the pre-expansion compensation model is called, and the basic geometric units and the transition connection structures therein are respectively extracted as two component sets through the graphic element grouping function. Then, the two-dimensional geometric attributes of the two types of structures are respectively counted, including area, boundary length, main axis direction length, and the relative connection boundary length between the structures. Then, taking the total structural area as the normalization reference, the proportion of each type of structure in the original model is calculated and output as the standard structural proportion characteristics.

[0045] Subsequently, after mapping and combining M translation cyclic splicing structures and M groups of rotation cyclic splicing structures, M groups of real-time structural proportion characteristics are calculated and output. This step adopts the structural splicing analysis method, which combines the M translation cyclic splicing structures and the M groups of rotation cyclic splicing structures in sequence to form M groups of nested combined structures. Using the geometric measurement tool in the CAD platform, graphic recognition is performed on the basic geometric units and the transition connection structures in each group of structural combinations, and area, side length, and joint boundary length measurements are performed. Then, using the normalization processing method, the measured values of each structural category are converted into their relative proportion values in this combined structure, so as to calculate M groups of real-time structural proportion characteristics.

[0046] Finally, according to whether the M groups of real-time structural proportion characteristics meet the standard structural proportion characteristics, multiple candidate cyclic splicing structures are screened out. This step adopts the quantity proportion consistency determination method to simplify the ratio of the M groups of real-time structure proportions to the standard structure proportions and judge whether they form the same proportion relationship with the standard proportion. For example, if the standard structure proportion is 2:3, and the quantity proportion of a certain group of splicing structures is 4:6, it is considered to meet the requirement; if it is 5:8, it does not meet the requirement. To consider the possible differences in the number of structures in the actual splicing edges or corner residual spaces, a limited integer difference tolerance range is set, that is, if the quantity proportion error between the basic geometric unit and the transition connection structure is within ±1 structural unit (for example, it should be 4:6, but actually it is 4:5 or 4:7), it can also be considered to approximately meet the standard proportion. Here, the ±1 unit means that the number of any type of structure and its expected proportion differ by at most 1 in the integer dimension. The splicing structures outside this range will be excluded. Finally, multiple candidate cyclic splicing structures are screened and output, and each candidate structure is composed of a group of translation cyclic splicing structures and a group of rotation cyclic splicing structures.

[0047] Step S400: Based on the curve path planning, fit and output multiple candidate cutting paths of the multiple candidate cyclic splicing structures.

[0048] In the embodiments of the present application, in order to convert multiple candidate loop splicing structures into path data that can be actually executed by thermal cutting, continuous and feasible curve trajectories are generated according to the structural boundary features, which are specifically implemented by means of fitting and outputting multiple candidate cutting paths of the multiple candidate loop splicing structures based on curve path planning.

[0049] Specifically, first, a boundary contour extraction method is adopted to perform two-dimensional contour recognition on each candidate loop splicing structure, and a graphic processing tool is used to perform line segment recognition on its outer contour and internal hole contour to obtain a continuous boundary point sequence constituting the basic geometric unit and the transition connection structure; then, a curve fitting method is adopted to perform fitting processing on the extracted boundary point sequence, converting it from multiple broken lines into a continuous smooth curve, mainly using B-spline fitting or least squares fitting to ensure good continuity and smoothness of the path during processing; then, corresponding cutting path data is generated for each fitted curve and output as a candidate path for the splicing structure. Finally, path generation is completed for each candidate loop splicing structure respectively to obtain multiple candidate cutting paths corresponding to it one by one.

[0050] Step S500: Calculate the material utilization rates of the multiple candidate loop splicing structures based on the dynamic nested boundary and the multiple candidate cutting paths.

[0051] In the embodiments of the present application, when calculating the material utilization rates of the multiple candidate loop splicing structures based on the dynamic nested boundary and the multiple candidate cutting paths, first, the dynamic nested boundary is rasterized according to a preset scale to construct a standard raster matrix; then, the multiple candidate loop splicing structures are projected onto this raster matrix to identify the first-level unused raster grids not covered by the effective structures; subsequently, the multiple candidate cutting paths are introduced to deduct the path gap areas in the first-level unused raster grids to obtain the distribution of the second-level unused raster grids; finally, the ratio of the total raster number of the dynamic nested boundary to the number of effectively reserved raster grids is calculated to output the material utilization rate corresponding to each group of candidate loop splicing structures. Through this process, the material utilization rates of the multiple candidate loop splicing structures are obtained.

[0052] Furthermore, in the method provided by the embodiments of the application, calculating the material utilization rates of the multiple candidate loop splicing structures based on the dynamic nested boundary and the multiple candidate cutting paths further includes: Discretize the dynamic nested boundary into a raster matrix of a preset scale; project the multiple candidate loop splicing structures onto the raster matrix to locate the distribution of multiple first-level unused raster grids; project the multiple candidate cutting paths onto the distribution of the multiple first-level unused raster grids to deduct the path gaps to obtain the distribution of multiple second-level unused raster grids; calculate the volume ratio of the dynamic nested boundary to the distribution of the multiple second-level unused raster grids and output the multiple material utilization rates.

[0053] In the embodiment of the present application, first, the dynamic nested boundary is discretized into a grid matrix of a preset scale. Using the regular grid division method, in a two-dimensional space, with a square of a fixed side length (e.g., 1 mm) as the unit, the dynamic nested boundary is divided into planes to construct a grid matrix covering the entire nested area.

[0054] After rasterization, multiple candidate loop splicing structures are projected onto the grid matrix to locate multiple first-level deprecated grid distributions. This step uses the structural graphic projection method to project the two-dimensional boundary information of each candidate loop splicing structure onto the grid matrix, and determines its occupancy status by judging whether the grid center falls within the structural contour range. The grids not covered by any candidate loop splicing structure are uniformly marked as first-level deprecated grid distributions, representing the areas not actually utilized in the structural layout.

[0055] Subsequently, multiple candidate cutting paths are continuously projected onto multiple first-level deprecated grid distributions for path gap deduction to obtain multiple second-level deprecated grid distributions. This step uses the path buffer zone deduction method. Based on the center line of each candidate cutting path, a cutting seam width (e.g., 0.8 mm) is extended outward to form the thermal cutting influence range, and this buffer range is projected onto the first-level deprecated grid distributions. If the path buffer range coincides with the first-level deprecated grid, this grid is marked as a second-level deprecated grid distribution, indicating material waste caused by the thermal influence or path gap of the cutting path.

[0056] Finally, the volume ratio of the dynamic nested boundary to multiple second-level deprecated grid distributions is calculated. This step uses the grid volume ratio calculation method. Taking the total number of grids in the dynamic nested boundary as the total area benchmark, subtracting the corresponding number of second-level deprecated grids in each group to obtain the number of effectively utilized grids, and calculating the ratio with the total number of grids to obtain the material utilization rate corresponding to each candidate loop splicing structure. Through this process, multiple material utilization rates are output.

[0057] Furthermore, in the method provided by the application embodiment, when calculating the volume ratio of the dynamic nested boundary to multiple second-level deprecated grid distributions and outputting the multiple material utilization rates, it further includes: Interactively obtaining the EVA padding design; using the EVA padding design to perform reuse calculation on the multiple second-level deprecated grid distributions to obtain multiple third-level deprecated grid distributions; calculating the volume ratio of the dynamic nested boundary to the multiple third-level deprecated grid distributions and outputting multiple updated material utilization rates; using the multiple updated material utilization rates to replace the material utilization rates for cutting characteristic analysis.

[0058] In the embodiments of the present application, first, an EVA padding design is obtained through interaction. The graphic template calling method is adopted to call a preset standard gasket template library through a human-computer interaction interface, or an operator manually imports a padding graphic file with dimension markings. The graphics include common shapes such as circles, anti-slip sheets, and rectangular filling blocks, which are used as subsequent reusable layout objects. The imported EVA padding design is converted into a set of nestable two-dimensional contours, and attributes such as dimension parameters, rotational directionality, and edge spacing limits are bound to facilitate layout.

[0059] After that, the EVA padding design is used to perform reuse calculations on multiple secondary deprecated grid distributions. By adopting the space nesting layout method, the EVA padding graphics are placed one by one in the idle areas corresponding to the secondary deprecated grids. Based on the spatial topological data of the secondary deprecated grids, the boundaries of continuous available areas are identified, and a heuristic nesting algorithm (such as the maximum empty rectangle method) is used to fill and layout the graphics according to the graphic size, rotation angle, and minimum margin rules. The areas where the padding graphics are successfully placed are marked as utilized areas, and the remaining idle grids that cannot be nested are reserved as tertiary deprecated grid distributions. Through this process, multiple tertiary deprecated grid distributions are obtained.

[0060] Next, the volume ratio of the dynamic nesting boundary to multiple tertiary deprecated grid distributions is calculated, and multiple updated material utilization rates are output. This process uses the discrete area difference ratio calculation method. First, the total number of all grids divided by the dynamic nesting boundary is counted as the number of benchmark area units, and then the number of each group of tertiary deprecated grids is counted. The updated material utilization rate is calculated and output through the formula: updated material utilization rate = 1 - number of tertiary deprecated grids / total number of grids. This ratio reflects the overall board utilization efficiency after the reuse of the main structure and EVA padding.

[0061] Finally, multiple updated material utilization rates are used to replace the material utilization rate for cutting characteristic analysis. The utilization rate-driven characteristic re-analysis method is adopted to update the index fields in the raw material utilization rate data table to the corresponding updated material utilization rates, and the cutting performance analysis associated with the candidate loop splicing structure is re-performed.

[0062] Step S600: Perform cutting characteristic analysis based on the multiple material utilization rates and the multiple candidate cutting paths to screen and select a target nested segmentation structure from the multiple candidate loop splicing structures.

[0063] In the embodiments of the present application, in order to screen out the target nested segmentation structure from multiple candidate cyclic splicing structures, first, each group of candidate cutting paths is run to fit the EVA lining cutting process, and a sequence of path turning radii, a sequence of cutting loads, and a sequence of cutting breakpoints that form the structural path features are extracted to comprehensively characterize path smoothness, energy consumption fluctuations, and cutting continuity. Subsequently, based on the sequence of cutting breakpoints, the proportion of continuous cutting segments is statistically calculated, the dispersion of the path turning radii is calculated through the turning angle data, and the load fluctuation coefficient is obtained for the load sequence, respectively reflecting the path interruption frequency, trajectory complexity, and cutting stability. Then, the above three path characteristic indicators are fused and mapped with the corresponding material utilization rate of each group to form a unified evaluation basis, that is, the candidate cutting availability coefficient. Finally, according to the serialized sorting result of this availability coefficient, the optimal target nested segmentation structure is screened out from all candidate cyclic splicing structures.

[0064] Further, in the method provided by the application embodiments, for cutting characteristic analysis based on the multiple material utilization rates and the multiple candidate cutting paths to screen out the target nested segmentation structure from the multiple candidate cyclic splicing structures, it further includes: Running the multiple candidate cutting paths for EVA lining cutting fitting, and outputting a plurality of sequences of path turning radii, a plurality of sequences of cutting loads, and a plurality of sequences of cutting breakpoints; calculating and outputting a plurality of proportions of continuous cutting segments based on the plurality of sequences of cutting breakpoints; calculating and outputting a plurality of dispersions of path turning radii based on the plurality of sequences of path turning radii; calculating a plurality of load fluctuation coefficients of the plurality of cutting load sequences; obtaining a plurality of candidate cutting availability coefficients through mapping and weighted fusion of the multiple material utilization rates, the multiple proportions of continuous cutting segments, the multiple dispersions of path turning radii, and the multiple load fluctuation coefficients; and mapping and calling the target nested segmentation structure from the multiple candidate cyclic splicing structures according to the serialized results of the plurality of candidate cutting availability coefficients.

[0065] In the embodiments of the present application, first, multiple candidate cutting paths are run for EVA lining cutting fitting. Specifically, the actual sample cutting method is adopted. Each group of candidate cutting paths is loaded onto the numerical control cutting equipment, and cutting operations are directly carried out on the EVA board sample. At the same time, the control system log and sensor acquisition data of the equipment are called. By reading the cutter head movement control instructions and position feedback values, all turning points in the path are extracted, and the circular arc transition curvature between adjacent path segments is calculated, and output as a plurality of sequences of path turning radii to characterize the smoothness of path changes. At the same time, the power feedback signal and running time are read, and the power per unit time multiplied by the segment length is calculated according to the path segment, and output as a plurality of sequences of cutting loads to record the heat energy input intensity of each path segment in actual processing. Combining the cutting head start-stop signal and position change data, the starting and stopping points of the cutter in the path are detected, and the positions of discontinuous paragraphs are identified, and output as a plurality of sequences of cutting breakpoints to describe the continuity of the path.

[0066] Subsequently, multiple continuous cutting segment ratios are calculated based on multiple cutting breakpoint sequences. Using the threshold segmentation method, each path is divided into several segments according to the cutting breakpoints, and valid cutting segments with a continuous length of not less than 50 mm are selected. The ratio of the total length of these segments to the length of the entire path is calculated and output as multiple continuous cutting segment ratios, which reflect the proportion of sustainable cutting segments in the path. Then, multiple path corner radius dispersions are calculated based on multiple path corner radius sequences. Using the standard deviation statistical method, variance analysis is performed on each set of path corner radius data to calculate the consistency of its change range, and the output is multiple path corner radius dispersions, which are used to evaluate the smoothness of corner changes in the path.

[0067] Immediately afterwards, multiple load fluctuation coefficients of multiple cutting load sequences are calculated. Using the mean-standard deviation ratio method, the mean and standard deviation of each set of cutting load sequences are calculated, and the multiple load fluctuation coefficients are obtained by dividing the standard deviation by the mean. These coefficients are used to measure the stability of the thermal load. The lower this value, the more uniform the power output and the more stable the temperature rise control during the processing.

[0068] After completing the above feature extraction, through mapping and weighted fusion of the multiple material utilization rates, multiple continuous cutting segment ratios, multiple path corner radius dispersions, and multiple load fluctuation coefficients, using the method of normalized scoring and weighted summation, the four indicators are uniformly converted into scoring values between 0 and 1. Combining the weight ratios (for example, material utilization rate 30%, continuous cutting segment ratio 30%, path corner radius dispersion 20%, load fluctuation coefficient 20%), the candidate cutting availability coefficient of each set of structures is comprehensively obtained, which is used to reflect the overall applicability of the structure in actual processing.

[0069] Finally, according to the serialized results of multiple candidate cutting availability coefficients, the target nested segmentation structure is mapped and called from multiple candidate cyclic splicing structures. Using the sorting and screening method, all candidate structures are arranged in descending order of the availability coefficient, and the structure with the highest score is selected as the target nested segmentation structure for the final EVA inner lining cutting execution.

[0070] Step S700: Use the target nested segmentation structure to perform cyclic segmentation of the EVA inner lining for the toolbox.

[0071] In the embodiment of the present application, after determining the target nested segmentation structure, the cyclic segmentation of the EVA lining for the tool box is performed using the target nested segmentation structure. Specifically, the cutting path data corresponding to the basic geometric units and the transition connection structures included in the target nested segmentation structure is sent to the numerical control cutting equipment, and the equipment is controlled to process on the EVA board along the preset path trajectory. The cutting equipment performs path execution group by group according to the arrangement mode of the target nested segmentation structure within the dynamic nested boundary, and repeats the arrangement on the raw material board and completes the segmentation of multiple structural units. Through the continuous execution of the target nested segmentation structure, the cyclic segmentation processing of the entire EVA raw material area is realized, and the batch forming of the tool box lining structure is completed.

[0072] In the embodiment of the present application, in summary, the embodiment of the present application has at least the following technical effects: Based on the non-linear shrinkage of the thermal cutting material, the present application performs reverse dimensional compensation on the tool box lining to obtain a pre-expansion compensation model; interactively obtains the raw material design information of the raw material board, and constructs a dynamic nested boundary according to the raw material design information; performs periodic nested splicing fitting of the pre-expansion compensation model within the dynamic nested boundary to obtain multiple candidate cyclic splicing structures; based on the curved path planning fitting, outputs multiple candidate cutting paths of the multiple candidate cyclic splicing structures; calculates the material utilization rates of the multiple candidate cyclic splicing structures according to the dynamic nested boundary and the multiple candidate cutting paths; performs cutting characteristic analysis according to the multiple material utilization rates and the multiple candidate cutting paths to screen out the target nested segmentation structure from the multiple candidate cyclic splicing structures; uses the target nested segmentation structure to perform cyclic segmentation of the EVA lining for the tool box. The present invention solves the technical problems of rough typesetting layout and low material utilization rate in the prior art. By importing the lining shape data and the raw material size data, combining with the optimization algorithm for periodic nested arrangement and cutting path fitting, and dynamically adjusting the cutting process parameters, the technical effects of improving the material utilization rate and reducing the waste rate are achieved.

[0073] Embodiment 2, based on the same inventive concept as the cutting optimization method for an EVA lining for a tool box in the foregoing embodiment, as Figure 2 shown, the present application provides a cutting optimization system for an EVA lining for a tool box. The system in the embodiment of the present application and the method embodiment are based on the same inventive concept. Among them, the system includes: The reverse dimension compensation module 11 is used to perform reverse dimension compensation on the inner lining of the tool box based on the non-linear shrinkage of the thermally cut material to obtain a pre-expansion compensation model; the nested boundary construction module 12 is used to interactively obtain the raw material design information of the raw material plate and construct a dynamic nested boundary according to the raw material design information; the splicing and fitting module 13 is used to perform periodic nested splicing and fitting of the pre-expansion compensation model within the dynamic nested boundary to obtain a plurality of candidate cyclic splicing structures; the cutting path determination module 14 is used to fit and output a plurality of candidate cutting paths of the plurality of candidate cyclic splicing structures based on the curve path planning; the utilization rate calculation module 15 is used to calculate the material utilization rates of the plurality of candidate cyclic splicing structures according to the dynamic nested boundary and the plurality of candidate cutting paths; the cutting characteristic analysis module 16 is used to perform cutting characteristic analysis according to the plurality of material utilization rates and the plurality of candidate cutting paths to screen out a target nested segmentation structure from the plurality of candidate cyclic splicing structures; the cyclic segmentation module 17 is used to perform cyclic segmentation of the EVA inner lining for the tool box by using the target nested segmentation structure.

[0074] Furthermore, the system is also used to implement the following functions: Construct an inner lining design model according to the inner lining structure design parameters of the EVA inner lining of the tool box; locally call the cutting temperature field distribution, and establish a non-linear shrinkage model according to the thermal expansion coefficient of the EVA material to be cut and the cutting temperature field distribution; use the non-linear shrinkage model to perform reverse dimension compensation on the inner lining design model to generate the pre-expansion compensation model.

[0075] Furthermore, the system is also used to implement the following functions: Locally call the edge tolerance correlation index, where the edge tolerance correlation index includes the cutting equipment accuracy and the clamping mechanism error; after constructing a raw material design model according to the raw material design information, delimit a rectangular effective processing area from the raw material design model according to the edge tolerance correlation index; interactively obtain the cutting edge boundary characteristics, and construct a thermal deformation buffer zone according to the cutting edge boundary characteristics; superimpose the thermal deformation buffer zone on the rectangular effective processing area to obtain the dynamic nested boundary.

[0076] Furthermore, the system is also used to implement the following functions: Use a linear array CCD camera to scan the surface of the raw material plate to locate the distribution of multiple surface defects; according to the size characteristics of the distribution of the multiple surface defects, match a circular disabled grid; use the circular disabled grid to perform center coverage with the distribution centers of the multiple surface defects, and perform global calibration of the disabled grid within the dynamic nested boundary.

[0077] Furthermore, the system is also used to implement the following functions: Perform topological analysis on the pre-expansion compensation model to extract basic geometric units and transitional connection structures; predefined nested repetition rules, where the nested repetition rules consist of translational repetition restrictions and rotational repetition restrictions; according to the translational repetition restrictions, perform two-way equidistant replication of the basic geometric units along the Y-axis direction and the X-direction on the dynamic nested boundary until nested loops, and output M translational loop splicing structures; in the M misaligned spatial distributions of the dynamic nested boundary and the M translational loop splicing structures, perform misaligned replication of the transitional connection structures based on the rotational repetition restrictions until nested loops, and output M sets of rotational loop splicing structures; map and combine the M translational loop splicing structures and the M sets of rotational loop splicing structures to obtain the multiple candidate loop splicing structures.

[0078] Further, the system is also used to implement the following functions: Restore the basic geometric units and transitional connection structures to the pre-expansion compensation model to calculate the standard structure ratio characteristics; after mapping and combining the M translational loop splicing structures and the M sets of rotational loop splicing structures, calculate and output M sets of real-time structure ratio characteristics; according to whether the M sets of real-time structure ratio characteristics meet the standard structure ratio characteristics, screen to obtain the multiple candidate loop splicing structures, where each candidate loop splicing structure consists of a translational loop splicing structure and a rotational loop splicing structure.

[0079] Further, the system is also used to implement the following functions: Discretize the dynamic nested boundary into a grid matrix of a preset scale; project the multiple candidate loop splicing structures onto the grid matrix to locate multiple first-level deprecated grid distributions; project the multiple candidate cutting paths onto the multiple first-level deprecated grid distributions to deduct path gaps, and obtain multiple second-level deprecated grid distributions; calculate the volume ratio of the dynamic nested boundary to the multiple second-level deprecated grid distributions, and output the multiple material utilization rates.

[0080] Further, the system is also used to implement the following functions: Interactively obtain the EVA padding design; use the EVA padding design to perform reuse calculations on the multiple second-level deprecated grid distributions to obtain multiple third-level deprecated grid distributions; calculate the volume ratio of the dynamic nested boundary to the multiple third-level deprecated grid distributions, and output multiple updated material utilization rates; use the multiple updated material utilization rates to replace the material utilization rates for cutting characteristic analysis.

[0081] Further, the system is also used to implement the following functions: Run the multiple candidate cutting paths for EVA inner lining cutting and fitting, and output multiple path corner radius sequences, multiple cutting load sequences, and multiple cutting breakpoint sequences; calculate and output multiple continuous cutting segment ratios based on the multiple cutting breakpoint sequences; calculate and output multiple path corner radius dispersions based on the multiple path corner radius sequences; calculate multiple load fluctuation coefficients of the multiple cutting load sequences; obtain multiple candidate cutting availability coefficients through mapped weighted fusion of the multiple material utilization rates, multiple continuous cutting segment ratios, multiple path corner radius dispersions, and multiple load fluctuation coefficients; and map and call the target nested segmentation structure from the multiple candidate cyclic splicing structures according to the serialization results of the multiple candidate cutting availability coefficients.

[0082] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0083] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0084] This specification and the drawings are only exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A cutting optimization method for an EVA lining of a tool box, characterized in that, The method includes: Performing reverse dimensional compensation on the inner lining of the tool box based on the non-linear shrinkage of the thermally cut material to obtain a pre-expansion compensation model; Interactively obtaining the raw material design information of the raw material sheet and constructing a dynamic nested boundary according to the raw material design information; Performing periodic nested splicing fitting of the pre-expansion compensation model within the dynamic nested boundary to obtain multiple candidate cyclic splicing structures; Fitting and outputting multiple candidate cutting paths of the multiple candidate cyclic splicing structures based on curve path planning; Calculating the material utilization rates of the multiple candidate cyclic splicing structures according to the dynamic nested boundary and the multiple candidate cutting paths; Performing cutting characteristic analysis according to the multiple material utilization rates and the multiple candidate cutting paths to screen out the target nested segmentation structure from the multiple candidate cyclic splicing structures; Performing cyclic segmentation of the EVA inner lining for the tool box using the target nested segmentation structure.

2. The cutting optimization method of the EVA lining for a tool box according to claim 1, characterized in that, Performing reverse dimensional compensation on the inner lining of the tool box based on the non-linear shrinkage of the thermally cut material to obtain a pre-expansion compensation model, the method including: Constructing an inner lining design model according to the inner lining structure design parameters of the tool box EVA inner lining; Locally calling the cutting temperature field distribution and establishing a non-linear shrinkage model according to the thermal expansion coefficient of the EVA material to be cut and the cutting temperature field distribution; Performing reverse dimensional compensation on the inner lining design model using the non-linear shrinkage model to generate the pre-expansion compensation model.

3. The cutting optimization method of an EVA lining for a tool box according to claim 1, characterized in that, Interactively obtaining the raw material design information of the raw material sheet and constructing a dynamic nested boundary according to the raw material design information, the method including: Locally calling the edge tolerance correlation index, where the edge tolerance correlation index includes cutting equipment accuracy and clamping mechanism error; After constructing a raw material design model according to the raw material design information, demarcating a rectangular effective processing area from the raw material design model according to the edge tolerance correlation index; Interactively obtaining the cutting edge boundary feature and constructing a thermal deformation buffer zone according to the cutting edge boundary feature; Superimposing the thermal deformation buffer zone on the rectangular effective processing area to obtain the dynamic nested boundary.

4. The cutting optimization method of the EVA lining for a tool box according to claim 3, characterized in that, The method further includes: Scanning the surface of the raw material sheet using a linear array CCD camera to locate the distribution of multiple surface defects; Matching circular disabled grids according to the size characteristics of the multiple surface defect distributions; Using the circular disabled grids to perform center coverage with the multiple surface defect distributions as the centers, and performing global calibration of the disabled grids within the dynamic nested boundary.

5. The cutting optimization method of the EVA lining for a tool box according to claim 1, characterized in that, Performing periodic nested splicing fitting of the pre-expansion compensation model within the dynamic nested boundary to obtain multiple candidate cyclic splicing structures, the method including: Performing topological analysis on the pre-expansion compensation model to extract basic geometric units and transitional connection structures; Pre-defining nested repetition rules, where the nested repetition rules are composed of translation repetition restrictions and rotation repetition restrictions; According to the translation repetition restrictions, performing two-way equidistant replication of the basic geometric units along the Y-axis direction and the X-direction within the dynamic nested boundary until nested cycling, and outputting M translation cyclic splicing structures; Based on the M misaligned spatial distributions of the dynamic nested boundary and the M translational cyclic splicing structures, perform misaligned replication of the transition connection structure according to the rotation repetition limit until nested loops, and output M sets of rotational cyclic splicing structures; Map and combine the M translational cyclic splicing structures and the M sets of rotational cyclic splicing structures to obtain the multiple candidate cyclic splicing structures.

6. The cutting optimization method of an EVA lining for a tool box according to claim 5, characterized in that, Map and combine the M translational cyclic splicing structures and the M sets of rotational cyclic splicing structures to obtain the multiple candidate cyclic splicing structures. The method includes: Restore the basic geometric unit and the transition connection structure to the pre-expansion compensation model to calculate the standard structure ratio characteristics; After mapping and combining the M translational cyclic splicing structures and the M sets of rotational cyclic splicing structures, calculate and output M sets of real-time structure ratio characteristics; According to whether the M sets of real-time structure ratio characteristics meet the standard structure ratio characteristics, screen to obtain the multiple candidate cyclic splicing structures, where each candidate cyclic splicing structure is composed of a translational cyclic splicing structure and a rotational cyclic splicing structure.

7. The cutting optimization method of an EVA lining for a tool box as described in claim 1, characterized in that, Based on the dynamic nested boundary and the multiple candidate cutting paths, calculate the material utilization rates of the multiple candidate cyclic splicing structures. The method includes: Discretize the dynamic nested boundary into a grid matrix of a preset scale; Project the multiple candidate cyclic splicing structures onto the grid matrix to locate the distributions of multiple first-level unused grids; Project the multiple candidate cutting paths onto the distributions of the multiple first-level unused grids to deduct the path gaps, and obtain the distributions of multiple second-level unused grids; Calculate the volume ratio of the dynamic nested boundary to the distributions of the multiple second-level unused grids, and output the multiple material utilization rates.

8. The cutting optimization method of an EVA lining for a tool box as described in claim 7, characterized in that, Calculate the volume ratio of the dynamic nested boundary to the distributions of the multiple second-level unused grids, and output the multiple material utilization rates. The method includes: Interactively obtain the EVA padding design; Use the EVA padding design to perform reuse calculation on the distributions of the multiple second-level unused grids to obtain the distributions of multiple third-level unused grids; Calculate the volume ratio of the dynamic nested boundary to the distributions of the multiple third-level unused grids, and output multiple updated material utilization rates; Use the multiple updated material utilization rates to replace the material utilization rates for cutting characteristic analysis.

9. The cutting optimization method of the EVA lining for a tool box according to claim 1, characterized in that, Based on the multiple material utilization rates and the multiple candidate cutting paths, perform cutting characteristic analysis to select the target nested segmentation structure from the multiple candidate cyclic splicing structures. The method includes: Run the multiple candidate cutting paths for EVA lining cutting fitting, and output multiple path corner radius sequences, multiple cutting load sequences, and multiple cutting breakpoint sequences; Based on the multiple cutting breakpoint sequences, calculate and output the proportions of multiple continuous cutting segments; Based on the multiple path corner radius sequences, calculate and output the dispersions of the multiple path corner radii; Calculate the load fluctuation coefficients of the multiple cutting load sequences; Through mapping and weighted fusion of the multiple material utilization rates, the proportions of the multiple continuous cutting segments, the dispersions of the multiple path corner radii, and the load fluctuation coefficients, obtain multiple candidate cutting availability coefficients; Based on the serialization result of the multiple candidate cutting availability coefficients, the target nested segmentation structure is mapped and called from the multiple candidate loop splicing structures.

10. A cutting optimization system for an EVA lining of a tool box, characterized in that, The system is used to execute a cutting optimization method for an EVA lining of a tool box according to any one of claims 1-9, and the system includes: A reverse dimension compensation module for performing reverse dimension compensation on the tool box lining based on the non-linear shrinkage of the thermally cut material to obtain a pre-expansion compensation model; A nested boundary construction module for interactively obtaining the raw material design information of the raw material sheet and constructing a dynamic nested boundary according to the raw material design information; A splicing fitting module for performing periodic nested splicing fitting of the pre-expansion compensation model within the dynamic nested boundary to obtain a plurality of candidate loop splicing structures; A cutting path determination module for fitting and outputting a plurality of candidate cutting paths of the plurality of candidate loop splicing structures based on the curved path planning; A utilization rate calculation module for calculating the material utilization rates of the plurality of candidate loop splicing structures according to the dynamic nested boundary and the plurality of candidate cutting paths; A cutting characteristic analysis module for performing cutting characteristic analysis according to the plurality of material utilization rates and the plurality of candidate cutting paths to screen and determine a target nested segmentation structure from the plurality of candidate loop splicing structures; A loop segmentation module for performing loop segmentation of the EVA lining of the tool box by using the target nested segmentation structure.

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