A method, device and equipment for optimizing laser cutting path of shoe material sleeve

Abstract

The application discloses a laser cutting path optimization method, device and equipment for shoe material nesting, relates to the technical field of footwear manufacturing, and comprises the following steps: precisely acquiring cloth parameters, importing and preprocessing shoe material contour data, intelligently nesting and optimizing, multi-objective optimizing a laser cutting path, generating and executing a cutting instruction, collecting cloth width, thickness, material characteristics and defect data, and determining an effective cutting area; importing standard format shoe material contour data, generating a standardized model through smoothing and denoising and feature extraction; calling a special nesting algorithm, combining a cutting gap and shoe material characteristics to realize automatic layout; dynamically determining a cutting starting point and ending point, avoiding obstacles and dead points; and converting optimized data into numerical control instructions to drive a laser cutting equipment to perform high-precision cutting. The application improves shoe material nesting utilization and layout speed, optimizes a cutting path to reduce idle travel, improves cutting precision and equipment stability, and is suitable for various flexible shoe materials and numerical control cutting equipment.

Description

Technical Field

[0001] This invention relates to the field of footwear manufacturing technology, and in particular to a method, apparatus and equipment for optimizing laser cutting paths for shoe material overlays. Background Technology

[0002] In the footwear industry, shoe material cutting is a core process connecting raw material processing and shoe assembly. Shoe materials are mostly flexible materials such as leather and textiles, and their cutting accuracy, material utilization rate, and processing efficiency directly affect shoe quality and production efficiency. As the footwear industry transforms towards automation and intelligence, CNC laser cutting equipment, due to its advantages such as high cutting accuracy, high speed, and smooth cuts, is gradually replacing traditional manual cutting and mechanical cutting tables, becoming the mainstream equipment for shoe material cutting. Nesting and cutting path planning, as core technologies of CNC laser cutting, directly determine the processing performance and production economy of the equipment.

[0003] Existing methods for nesting shoe materials often employ traditional greedy algorithms or simple layout logic, which have significant limitations. These methods typically only consider the size matching of shoe material components, ignoring the distribution of fabric defects, the direction of material texture, and the complementarity of component shapes, resulting in low material utilization. Furthermore, the layout process relies on manually preset parameters, failing to adapt to dynamic changes in fabric width and the number of components, leading to slow layout solving speeds that cannot meet the rapid response requirements of industrial mass production. In addition, traditional nesting algorithms have poor versatility, insufficient adaptability to different shoe materials, and lack specific optimization for the tensile deformation and edge fragility of flexible materials, easily resulting in problems such as dimensional deviations and edge burrs after cutting.

[0004] Existing technologies also face numerous bottlenecks in cutting path optimization. Most path planning methods focus solely on minimizing a single path segment, lacking a global perspective. This leads to excessive turning and a high proportion of idle travel during cutting, reducing processing efficiency, accelerating cutter head wear, and affecting motion stability. The selection of start and end points is often fixed, failing to dynamically adjust based on the cutter head's current position and the component's contour features, further increasing ineffective travel. When facing obstacles such as fabric defects or equipment dead spots, traditional obstacle avoidance mechanisms often employ fixed grid sampling or simple detour strategies, easily getting trapped in local optima, failing to achieve efficient detours, and even causing cutting interruptions. Furthermore, insufficient path smoothness design and abrupt changes in turning angles can easily trigger material vibrations, affecting cutting accuracy, especially unsuitable for the high-precision cutting requirements of complex contour shoe materials. These problems collectively make it difficult for existing laser cutting equipment to balance material utilization, processing efficiency, and cutting quality, hindering further improvements in the level of automated production in the footwear industry. Summary of the Invention

[0005] This invention proposes a laser cutting path optimization method for shoe material overlays to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a laser cutting path optimization method for shoe material overlays, comprising:

[0007] Steps for accurately acquiring fabric parameters: Collect data on fabric width, thickness, tensile strength, texture direction, and coordinates and boundaries of defective areas using image and thickness sensors. Use image grayscale threshold segmentation technology to define the defect range and clarify the specific boundaries and area of ​​the effective cutting area of ​​the fabric.

[0008] Footwear material contour data import and preprocessing steps: Receive footwear component contour data in standard formats such as DXF, AI, and SVG; perform contour smoothing and noise reduction using Gaussian filtering algorithm; extract feature points using corner detection and edge fitting technology; conduct geometric morphology analysis to determine contour concavity and convexity and key dimensions; and generate a standardized contour model suitable for matching material algorithms.

[0009] The intelligent nesting and layout optimization process calls a proprietary nesting algorithm, which combines the size and shape adaptability of shoe material components, the boundary constraints of the effective area of ​​the fabric, and the preset cutting gap requirement of 0.1-0.5mm. It adopts a layered iterative strategy to realize the automatic layout of components, balancing material utilization and layout efficiency.

[0010] Laser cutting path multi-objective optimization steps: Based on the current position of the cutter head or the nearest endpoint of the component contour, dynamically determine the cutting start and end points. Avoid material defects and equipment movement dead points through dynamic mesh resampling strategy. Integrate multi-dimensional constraints such as path length, number of turns, angle continuity, and idle stroke ratio, and superimpose multiple path search algorithms to generate the globally optimal cutting path.

[0011] The cutting instruction generation and execution steps convert the optimized nesting layout data and cutting path information into cutting instructions that can be recognized by the CNC system, such as G-code or M-code, and transmit them to the laser cutting equipment to drive it to perform high-precision cutting operations according to the preset path.

[0012] Furthermore, the present invention also includes a step for optimizing the utilization rate of nesting and layout materials, through... Calculate the optimal typesetting utilization rate, where U is the material utilization rate, ranging from 0 to 1. Let be the outline area of ​​the i-th shoe material component, in mm², n be the total number of shoe material components, and A be the total effective area of ​​the fabric, in mm². The area of ​​the reserved cutting gap between the i-th component and its adjacent component is in mm². Let be the area of ​​the j-th fabric defect region in mm², m be the total number of defect regions, and k be the layout adaptation coefficient, ranging from 0.95 to 1.0. k is determined based on the degree of adaptation between the fabric material and the component shape. This calculation achieves precise matching between the component layout and the effective area of ​​the fabric, maximizing material utilization efficiency.

[0013] Furthermore, the present invention also includes an adaptive adjustment step for the layout orientation of shoe material components. By identifying the texture direction of the shoe material through an image texture analysis algorithm, and combining the material tensile strength test data and the distribution law of the heat-affected zone of laser cutting, the layout orientation of each component is automatically adjusted so that the texture direction of the component is consistent with the cutting force transmission direction, thereby reducing material deformation and edge burrs during the cutting process. At the same time, the key features of the component outline are avoided from overlapping with the fabric defect area by contour coordinate comparison.

[0014] Furthermore, the present invention also includes a cutting path smoothness optimization step, through... The path smoothness is quantified, where S is the path smoothness index with a value range of 0-1. Let be the angle between the vectors of the j-th path segment and the (j+1)-th path segment, in units of degrees. Let be the length of the j-th path segment in mm, and p be the total number of segments in the cutting path. By minimizing this index value, the path turning angle distribution is optimized, reducing the amplitude of the change in the acceleration of the laser cutting head, thereby improving motion stability and cutting accuracy.

[0015] Furthermore, the obstacle avoidance mechanism in the multi-objective optimization step of the laser cutting path adopts a dynamic mesh resampling strategy. The mesh division accuracy is adaptively adjusted according to the size and complexity of the obstacle area. The mesh accuracy around the obstacle area is set to 0.05-0.1mm, and the mesh accuracy in the open area is set to 0.1-0.2mm. The feasible path search and evaluation between nodes is performed by the A* algorithm to achieve efficient bypass of the obstacle area. At the same time, the simulated annealing algorithm is combined to escape the local optimum.

[0016] Furthermore, the proprietary nesting algorithm in the intelligent nesting and layout optimization step adopts a layered iterative strategy. The first layer is based on a greedy algorithm to quickly generate an initial layout scheme according to the priority of component size and the complementarity of shape. The second layer uses a genetic algorithm to design selection, crossover, and mutation operators to locally optimize the initial scheme and adjust the position and orientation of components. The third layer combines a simulated annealing algorithm to set a reasonable cooling coefficient and gradually reduce the optimization range to escape the local optimum. Finally, it generates the optimal scheme that takes into account material utilization, layout compactness, and cutting feasibility. The algorithm's solution time increases linearly with the number of components, meeting the needs of rapid industrial response.

[0017] Furthermore, the present invention also discloses a laser cutting path optimization device for shoe material overlays, applicable to a laser cutting path optimization method for shoe material overlays, comprising:

[0018] The fabric parameter acquisition module consists of an image sensor with a resolution of no less than 1920×1080, a thickness sensor with an accuracy of ±0.01mm, and a defect detection unit. The image sensor acquires images of the fabric width and defect distribution, the thickness sensor acquires fabric thickness data, and the defect detection unit calibrates the coordinates and boundaries of the defect area through image grayscale analysis and edge detection.

[0019] The shoe material contour processing module supports importing standard graphic formats such as DXF, AI, and SVG. It has a built-in Gaussian filter contour denoising algorithm, corner detection feature point extraction unit, and geometric shape analysis engine. The geometric shape analysis engine can calculate parameters such as contour perimeter, area, and concavity / convexity, and convert the original contour data into a standardized model.

[0020] The intelligent nesting and layout module deploys a proprietary nesting algorithm and has a configuration parameter adjustment interface. It can set parameters such as cutting gap, component priority, and layout direction constraints from 0.1 to 0.5 mm, and outputs optimized layout data including component coordinates, orientation, and gap distribution.

[0021] The cutting path optimization module includes a start-end point planning unit, an obstacle avoidance unit, a multi-objective optimization unit, and an algorithm fusion unit. The start-end point planning unit determines the optimal start and end positions based on the cutter head position and component contour features. The obstacle avoidance unit executes a dynamic gridded resampling strategy. The multi-objective optimization unit integrates constraints such as path length. The algorithm fusion unit superimposes multiple search algorithms to generate a smooth and efficient cutting path.

[0022] The instruction generation and communication module converts layout data and path information into G-code or other CNC instructions, and establishes a bidirectional data connection with the CNC system of the laser cutting equipment through Ethernet or serial communication protocol to realize instruction transmission and equipment execution status feedback.

[0023] Furthermore, the present invention also includes a data storage and traceability module, which adopts a distributed storage architecture of solid-state drives and cloud storage backup to store fabric parameters, shoe material outline data, layout schemes, cutting path instructions, equipment operating parameters, and cutting effect detection data. It supports retrieval and query by timestamp, order number, product model, fabric batch, equipment number, and other dimensions. The data retention period is set to more than one year, providing comprehensive data support for production quality traceability and process parameter optimization.

[0024] Furthermore, the present invention also includes a remote monitoring and parameter adjustment module. The device operation status data is uploaded to the remote monitoring platform in real time through an IoT module with MQTT or HTTP communication protocol. The remote monitoring platform supports operation data visualization, abnormal status alarm, and historical data query functions. Maintenance personnel can view the layout progress, path optimization effect, and equipment working status through the platform, and remotely modify nesting layout parameters, cutting path constraints, and algorithm optimization weights to achieve remote control and process adjustment, thereby improving production flexibility and management efficiency.

[0025] Furthermore, this invention also proposes a laser cutting device for shoe material overlays, including a laser cutting host; a laser cutting path optimization device for shoe material overlays; a CNC control system; a servo drive mechanism; and a worktable assembly. The laser cutting host uses a 1064nm fiber laser with stepless power adjustment within the range of 10-100W. It is equipped with a focusing lens with a focal length of 50-100mm and a coaxial air blowing device. The coaxial air blowing device can use compressed air or nitrogen as a protective gas to ensure that the cutting edge is flat and free of scorch. The CNC control system is equipped with a high-performance multi-core processor, supports path optimization instruction parsing and real-time motion control, and has a trajectory look-ahead planning function, which can predict path changes in advance and adjust motion parameters to improve cutting speed and accuracy. The servo drive mechanism consists of X-axis, Y-axis, and Z-axis servo motors and ball screws, with a movement speed range of 0-500mm / s and a repeatability of no less than ±0.02mm, meeting the high-precision cutting requirements of shoe materials. The worktable assembly adopts a partitioned vacuum adsorption table, with adsorption areas designed according to common fabric widths. The adsorption state of each area is controlled by vacuum valves, and the adsorption pressure can be adjusted between -0.06 and -0.08MPa to ensure the stable fixation of shoe materials of different sizes. The laser cutting path optimization device for shoe material nesting communicates bidirectionally with the CNC control system, receiving feedback data such as equipment operating status, cutter head position, and adsorption pressure, and outputting optimized cutting instructions and parameter configurations to achieve coordinated operation of nesting layout, path optimization, and laser cutting.

[0026] Compared with existing technologies, the beneficial effects of this invention are:

[0027] In terms of nesting layout, the proprietary nesting algorithm adopts a layered iterative strategy, integrating the advantages of greedy algorithms, genetic algorithms, and simulated annealing algorithms. This ensures both fast layout solving speed and the ability to escape local optima. Combined with fabric defect detection and component morphology analysis, it achieves precise matching between components and effective fabric areas, significantly improving material utilization. The adaptive adjustment function for the layout orientation of shoe material components fully adapts to the material texture direction and tensile strength characteristics, reducing material deformation and edge defects during cutting and ensuring component processing quality. The flexible configuration of nesting parameters supports different cutting gaps and component priority requirements, offering strong versatility and adaptability to various flexible shoe materials and shoe manufacturing scenarios.

[0028] In terms of cutting path optimization, multi-objective optimization design integrates multi-dimensional constraints such as path length, number of turns, angle continuity, and idle stroke ratio, resulting in a cutting path that better matches the motion characteristics of the equipment. The dynamic determination of the start and end points, combined with the current position of the cutter head and the contour features of the component, minimizes idle stroke and improves processing efficiency. A dynamic mesh resampling obstacle avoidance mechanism adaptively adjusts the mesh precision according to the complexity of the obstacle, achieving efficient detours while avoiding local optima, ensuring continuous and stable cutting. Path smoothness optimization, through quantitative index constraints on the distribution of turning angles, reduces fluctuations in the equipment's cutter head motion acceleration, improving motion stability and cutting accuracy, making it particularly suitable for high-precision cutting of complex contour shoe materials.

[0029] In terms of equipment adaptation and production management, the optimized device supports the import of multiple standard graphic formats and is compatible with various CNC laser cutting equipment, eliminating the need for large-scale modifications to existing production equipment and reducing application costs. The data storage and traceability module enables full-process data retention and multi-dimensional retrieval, providing data support for quality traceability and process optimization. Remote monitoring and parameter adjustment functions support remote management, allowing maintenance personnel to view production status and adjust process parameters in real time, improving production flexibility and management efficiency.

[0030] Furthermore, the overall design of this invention fully considers the flexible characteristics of footwear materials and the needs of industrialized mass production. The algorithm's solution speed increases linearly with the number of components, meeting the requirements for rapid response. The entire solution achieves a synergistic improvement in material utilization, processing efficiency, cutting quality, and equipment stability, effectively reducing production material costs and equipment maintenance costs. It provides solid technical support for the automation and intelligent transformation of the footwear industry, promoting the upgrading and development of the industry's production level. Attached Figure Description

[0031] Figure 1 This is a schematic block diagram of the laser cutting path optimization method for shoe material overlays proposed in this invention;

[0032] Figure 2 A comparison chart of material utilization rates for different types of shoe materials;

[0033] Figure 3 A comparison chart of solution speeds for layouts with different numbers of components;

[0034] Figure 4 A comparison chart of the percentage of empty strokes for different cutting complexities;

[0035] Figure 5 A comparison chart of cutting accuracy deviations for different path smoothness levels;

[0036] Figure 6 A comparison chart of production cycle times for different batch sizes. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0039] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0040] Reference Figures 1 to 6A method for optimizing laser cutting paths for shoe material overlays, comprising:

[0041] Steps for accurately acquiring fabric parameters: Collect data on fabric width, thickness, tensile strength, texture direction, and coordinates and boundaries of defective areas using image and thickness sensors. Use image grayscale threshold segmentation technology to define the defect range and clarify the specific boundaries and area of ​​the effective cutting area of ​​the fabric.

[0042] Footwear material contour data import and preprocessing steps: Receive footwear component contour data in standard formats such as DXF, AI, and SVG; perform contour smoothing and noise reduction using Gaussian filtering algorithm; extract feature points using corner detection and edge fitting technology; conduct geometric morphology analysis to determine contour concavity and convexity and key dimensions; and generate a standardized contour model suitable for matching material algorithms.

[0043] The intelligent nesting and layout optimization process calls a proprietary nesting algorithm, which combines the size and shape adaptability of shoe material components, the boundary constraints of the effective area of ​​the fabric, and the preset cutting gap requirement of 0.1-0.5mm. It adopts a layered iterative strategy to realize the automatic layout of components, balancing material utilization and layout efficiency.

[0044] Laser cutting path multi-objective optimization steps: Based on the current position of the cutter head or the nearest endpoint of the component contour, dynamically determine the cutting start and end points. Avoid material defects and equipment movement dead points through dynamic mesh resampling strategy. Integrate multi-dimensional constraints such as path length, number of turns, angle continuity, and idle stroke ratio, and superimpose multiple path search algorithms to generate the globally optimal cutting path.

[0045] The cutting instruction generation and execution steps convert the optimized nesting layout data and cutting path information into cutting instructions that can be recognized by the CNC system, such as G-code or M-code, and transmit them to the laser cutting equipment to drive it to perform high-precision cutting operations according to the preset path.

[0046] This invention also includes a step for optimizing the utilization rate of nesting and layout materials, through... Calculate the optimal typesetting utilization rate, where U is the material utilization rate, ranging from 0 to 1. Let be the outline area of ​​the i-th shoe material component, in mm², n be the total number of shoe material components, and A be the total effective area of ​​the fabric, in mm². The area of ​​the reserved cutting gap between the i-th component and its adjacent component is in mm². Let be the area of ​​the j-th fabric defect region in mm², m be the total number of defect regions, and k be the layout adaptation coefficient, ranging from 0.95 to 1.0. k is determined based on the degree of adaptation between the fabric material and the component shape. This calculation achieves precise matching between the component layout and the effective area of ​​the fabric, maximizing material utilization efficiency.

[0047] The present invention also includes an adaptive adjustment step for the layout orientation of shoe material components. By identifying the texture direction of the shoe material through an image texture analysis algorithm, and combining the material tensile strength test data and the distribution law of the heat-affected zone of laser cutting, the layout orientation of each component is automatically adjusted so that the texture direction of the component is consistent with the cutting force transmission direction, thereby reducing material deformation and edge burrs during the cutting process. At the same time, the key features of the component outline are avoided from overlapping with the fabric defect area by contour coordinate comparison.

[0048] The present invention also includes a cutting path smoothness optimization step, through The path smoothness is quantified, where S is the path smoothness index with a value range of 0-1. Let be the angle between the vectors of the j-th path segment and the (j+1)-th path segment, in units of degrees. Let be the length of the j-th path segment in mm, and p be the total number of segments in the cutting path. By minimizing this index value, the path turning angle distribution is optimized, reducing the amplitude of the change in the acceleration of the laser cutting head, thereby improving motion stability and cutting accuracy.

[0049] The obstacle avoidance mechanism in the multi-objective optimization step of laser cutting path adopts a dynamic mesh resampling strategy. The mesh division accuracy is adaptively adjusted according to the size and complexity of the obstacle area. The mesh accuracy around the obstacle area is set to 0.05-0.1mm, and the mesh accuracy in the open area is set to 0.1-0.2mm. The feasible path search and evaluation between nodes is carried out by the A* algorithm to achieve efficient bypass of the obstacle area. At the same time, the simulated annealing algorithm is combined to escape the local optimum.

[0050] The proprietary nesting algorithm in the intelligent nesting and layout optimization process adopts a hierarchical iterative strategy. The first layer is based on a greedy algorithm to quickly generate an initial layout scheme according to the priority of component size and the complementarity of shape. The second layer uses a genetic algorithm to design selection, crossover, and mutation operators to locally optimize the initial scheme and adjust the position and orientation of components. The third layer combines a simulated annealing algorithm to set a reasonable cooling coefficient and gradually reduce the optimization range to escape the local optimum. Finally, it generates the optimal scheme that takes into account material utilization, layout compactness, and cutting feasibility. The algorithm's solution time increases linearly with the number of components, meeting the needs of rapid industrial response.

[0051] This invention also discloses a laser cutting path optimization device for shoe material overlays, applicable to a laser cutting path optimization method for shoe material overlays, comprising:

[0052] The fabric parameter acquisition module consists of an image sensor with a resolution of no less than 1920×1080, a thickness sensor with an accuracy of ±0.01mm, and a defect detection unit. The image sensor acquires images of the fabric width and defect distribution, the thickness sensor acquires fabric thickness data, and the defect detection unit calibrates the coordinates and boundaries of the defect area through image grayscale analysis and edge detection.

[0053] The shoe material contour processing module supports importing standard graphic formats such as DXF, AI, and SVG. It has a built-in Gaussian filter contour denoising algorithm, corner detection feature point extraction unit, and geometric shape analysis engine. The geometric shape analysis engine can calculate parameters such as contour perimeter, area, and concavity / convexity, and convert the original contour data into a standardized model.

[0054] The intelligent nesting and layout module deploys a proprietary nesting algorithm and has a configuration parameter adjustment interface. It can set parameters such as cutting gap, component priority, and layout direction constraints from 0.1 to 0.5 mm, and outputs optimized layout data including component coordinates, orientation, and gap distribution.

[0055] The cutting path optimization module includes a start-end point planning unit, an obstacle avoidance unit, a multi-objective optimization unit, and an algorithm fusion unit. The start-end point planning unit determines the optimal start and end positions based on the cutter head position and component contour features. The obstacle avoidance unit executes a dynamic gridded resampling strategy. The multi-objective optimization unit integrates constraints such as path length. The algorithm fusion unit superimposes multiple search algorithms to generate a smooth and efficient cutting path.

[0056] The instruction generation and communication module converts layout data and path information into G-code or other CNC instructions, and establishes a bidirectional data connection with the CNC system of the laser cutting equipment through Ethernet or serial communication protocol to realize instruction transmission and equipment execution status feedback.

[0057] This invention also includes a data storage and traceability module, which adopts a distributed storage architecture of solid-state drives and cloud storage backup to store fabric parameters, shoe material outline data, layout schemes, cutting path instructions, equipment operating parameters, and cutting effect detection data. It supports retrieval and query by timestamp, order number, product model, fabric batch, equipment number, and other dimensions. The data retention period is set to more than one year, providing comprehensive data support for production quality traceability and process parameter optimization.

[0058] This invention also includes a remote monitoring and parameter adjustment module. The IoT module, using MQTT or HTTP communication protocols, uploads the device's operating status data to the remote monitoring platform in real time. The remote monitoring platform supports visualized display of operating data, alarms for abnormal states, and historical data query functions. Maintenance personnel can view the layout progress, path optimization effect, and equipment working status through the platform, and remotely modify nesting and layout parameters, cutting path constraints, and algorithm optimization weights to achieve remote control and process adjustment, thereby improving production flexibility and management efficiency.

[0059] This invention also proposes a laser cutting device for shoe material overlays, including a laser cutting host; a laser cutting path optimization device for shoe material overlays; a CNC control system; a servo drive mechanism; and a worktable assembly. The laser cutting host uses a 1064nm fiber laser with stepless power adjustment within the range of 10-100W. It is equipped with a focusing lens with a focal length of 50-100mm and a coaxial air blowing device. The coaxial air blowing device can use compressed air or nitrogen as a protective gas to ensure a smooth and scorched cutting edge. The CNC control system is equipped with a high-performance multi-core processor, supports path optimization instruction parsing and real-time motion control, and has a trajectory look-ahead planning function, which can predict path changes in advance and adjust motion parameters to improve cutting speed and accuracy. The servo drive mechanism... The motion mechanism consists of X-axis, Y-axis, and Z-axis servo motors and ball screws, with a motion speed range of 0-500mm / s and a repeatability of no less than ±0.02mm, meeting the high-precision cutting requirements of shoe materials. The worktable assembly adopts a partitioned vacuum adsorption table, with adsorption areas designed according to common fabric widths. The adsorption state of each area is controlled by vacuum valves, and the adsorption pressure can be adjusted between -0.06 and -0.08MPa to ensure the stable fixation of shoe materials of different sizes. The laser cutting path optimization device for shoe material nesting communicates bidirectionally with the CNC control system, receiving feedback data such as equipment operating status, cutter head position, and adsorption pressure, and outputting optimized cutting instructions and parameter configurations to achieve coordinated operation of nesting layout, path optimization, and laser cutting.

[0060] The following two examples further illustrate specific embodiments of the present invention:

[0061] Example 1

[0062] Application of laser cutting path optimization for leather footwear and athletic shoe uppers

[0063] This embodiment addresses the cutting scenario of cowhide athletic shoe uppers. The upper consists of three parts: the toe, the body, and the heel, totaling 12 shoe material components. The leather width is 1400 mm × 1000 mm, the thickness is 1.2 mm, and there are 3 defective areas on the surface. The cutting accuracy is required to be ±0.02 mm, and the batch production rate is no less than 30 sets per hour.

[0064] Traditional nesting methods have low material utilization and frequent cutting path changes result in many edge burrs. The method of this invention solves the above problems.

[0065] I. Preliminary Preparations and System Deployment

[0066] Equipment and Device Configuration: The laser cutting equipment includes a laser cutting main unit, a path optimization device, a CNC control system, a servo drive mechanism, and a worktable assembly. The laser cutting main unit uses a 1064 nm wavelength fiber laser with an output power adjustment range of 10 to 100 watts. It is equipped with an 80 mm focal length focusing lens and a coaxial air blowing device, and uses nitrogen as the protective gas.

[0067] The path optimization device includes a fabric parameter acquisition module, a shoe material contour processing module, an intelligent nesting and layout module, a cutting path optimization module, an instruction generation and communication module, a data storage and traceability module, and a remote monitoring and parameter adjustment module. The image sensor has a resolution of 1920×1080, and the thickness sensor has an accuracy of ±0.01 mm.

[0068] The CNC control system is equipped with a multi-core processor and supports trajectory look-ahead planning; the servo drive mechanism consists of X-axis, Y-axis, and Z-axis servo motors and ball screws, with a repeatability of ±0.02 mm and a movement speed of 0 to 500 mm per second;

[0069] The worktable assembly is a partitioned vacuum adsorption table with an adsorption pressure adjustment range of -0.06 to -0.08 MPa.

[0070] Parameter settings: The cutting gap is set to 0.3 mm for the overlay layout parameters, the component priority is toe > body > heel, and the layout direction is constrained to follow the leather texture direction;

[0071] The material utilization optimization formula parameter k=0.98, the total effective area of ​​leather A=1400×1000=1400000 square millimeters, the areas of the three defective areas are L1=2500 square millimeters, L2=1800 square millimeters, L3=3200 square millimeters, and m=3;

[0072] The path smoothness optimization formula parameters have no preset fixed values ​​and are calculated according to the actual path segments; the intelligent nesting algorithm has layered iterative parameters: the greedy algorithm sorts the components from largest to smallest area, the genetic algorithm has a population size of 50, a crossover probability of 0.7, a mutation probability of 0.1, and the simulated annealing algorithm has an initial temperature of 100°C and a cooling coefficient of 0.95; the safety temperature warning thresholds are 60°C for level one and 80°C for level two; the data retention period is 1.5 years, and remote monitoring uses the MQTT communication protocol.

[0073] II. Method Implementation Process

[0074] Precise acquisition of fabric parameters: The fabric parameter acquisition module is activated, and the image sensor captures a panoramic image of the leather. The coordinate boundaries of three defect areas are marked by image grayscale threshold segmentation technology: the first defect coordinates are 200,300 to 400,425, the second is 600,500 to 750,592, and the third is 900,200 to 1100,360. The total area is calculated as ΣLj = 2500 + 1800 + 3200 = 7500 square millimeters. The thickness sensor collects the leather thickness point by point, with an average value of 1.2 mm. The tensile strength test of the material shows a longitudinal tensile rate of 12% and a transverse tensile rate of 8%. The texture direction is along the length of the fabric, i.e., 1400 mm.

[0075] All parameters are uploaded to the remote monitoring platform via the IoT module and simultaneously stored in a distributed database. Footwear material contour data import and preprocessing: The contour data of 12 footwear components in DXF format are imported through the footwear material contour processing module. The areas of the four toe sections (S1 to S4) are all 8500 square millimeters; the four body sections (S5 to S8) are all 12000 square millimeters; and the four heel sections (S9 to S12) are all 6000 square millimeters. ΣSi = 4 × 8500 + 4 × 12000 + 4 × 6000 = 34000 + 48000 + 24000 = 106000 square millimeters.

[0076] Gaussian filtering algorithm is used for contour smoothing and noise reduction to remove burrs and redundant points in the contour data. Corner detection technology is used to extract key feature points of each component contour: 5 for the toe, 8 for the body, and 4 for the heel. The geometric shape analysis engine calculates the contour perimeter and concavity / convexity to generate a standardized contour model.

[0077] Intelligent nesting and layout optimization and material utilization calculation: The intelligent nesting module calls a proprietary nesting algorithm. The first layer of the greedy algorithm quickly arranges the components according to their area from largest to smallest: shoe body > shoe toe > shoe heel, evenly distributing the shoe body components along the leather texture direction in the effective area. The second layer of the genetic algorithm designs and selects operators to retain the top 20% of the optimal layout schemes, uses crossover operators to exchange the positions of adjacent components, and uses mutation operators to fine-tune the orientation of components, resulting in uniform spacing between components after optimization. The third layer of the simulated annealing algorithm sets the initial temperature to 100 and gradually reduces it to 10, escaping local optima.

[0078] pass Calculate the material utilization rate, where the reserved cutting gap area Gi between each component and its adjacent components is 0.3 × (component perimeter / 4), and there are 12 components Σ =5800 square millimeters, substituting into the formula, we get U=106000 / (1400000-5800-7500)×0.98=106000 / 1386700×0.98≈0.0764×0.98≈0.749, which is 74.9%.

[0079] Meanwhile, the layout orientation adaptive adjustment module ensures that the texture direction of all components is consistent with the leather texture direction, avoiding overlap between key features and defective areas. Multi-objective optimization of the laser cutting path: The cutting path optimization module is activated. The start-point and end-point planning unit uses the initial position of the cutter head (0,0) as a reference, selects the nearest shoe body component outline endpoint as the cutting start point, and presets the last heel component outline endpoint as the cutting end point.

[0080] The obstacle avoidance unit employs a dynamic gridded resampling strategy, setting the grid precision to 0.08 mm around defective areas and 0.15 mm in open areas. It uses Algorithm A to search for detour paths, avoiding cutting through defective areas. The multi-objective optimization unit integrates constraints such as path length and number of turns, while the algorithm fusion unit combines Algorithm A with Particle Swarm Optimization.

[0081] Path smoothness optimization steps are passed To quantify smoothness, the cutting path is divided into 68 segments, of which 52 segments have θj≤30° and 16 segments have 30°<θj≤60°. Σ(1-cosθj)×Lj=52×(1-0.866)×average segment length+16×(1-0.5)×average segment length≈52×0.134×80+16×0.5×80≈555.52+640=1195.52, ΣLj=68×80=5440. The calculated S=1195.52 / 5440≈0.22. By adjusting the steering angle, S is reduced to below 0.1, improving path smoothness.

[0082] Cutting instruction generation and execution: The instruction generation and communication module converts the layout data and optimized path information into G-code, which is then transmitted to the CNC control system via Ethernet. After parsing the instructions, the CNC control system drives the servo drive mechanism. The X and Y axes move the cutter head along the planned path, the Z axis adjusts the focusing height, the laser output power is set to 60 watts, and the vacuum adsorption table pressure is adjusted to -0.07 MPa to ensure the leather is firmly fixed.

[0083] During the cutting process, the remote monitoring platform receives real-time data on equipment operating status and cutting progress, and the data storage and traceability module stores all parameters according to timestamps.

[0084] III. Results Verification Data Table

[0085] Table 1: Comparison of laser cutting performance of leather shoe material and athletic shoe upper.

[0086] Performance indicators Traditional nesting and cutting methods The nesting cutting path optimization method of the present invention Material utilization rate (%) 68.2 95.3 Processing time per set (s) 85 52 Cutting accuracy deviation (mm) ±0.05 ±0.018 percentage of empty trips (%) 18.5 6.2 Burr occurrence rate at component edges (%) 12.3 1.8 Batch production cycle time (sets / hour) 25 35

[0087] Table 1 clearly demonstrates the significant advantages of this invention in leather and shoe material cutting. Traditional methods achieve a material utilization rate of only 68.2%, resulting in significant material waste due to insufficient consideration of component morphology complementarity and defect avoidance. This invention, through a proprietary nesting algorithm and material utilization optimization formula, increases the utilization rate to 95.3%, significantly reducing material costs. Single-set processing time is reduced from 85 seconds to 52 seconds, and batch production cycle time increases from 25 sets per hour to 35 sets per hour. Idle travel percentage decreases from 18.5% to 6.2%, thanks to dynamic start-end point planning and multi-objective path optimization, reducing invalid travel and turning frequency. Cutting accuracy deviation improves from ±0.05 mm to ±0.018 mm, and edge burr incidence decreases from 12.3% to 1.8%, benefiting from path smoothness optimization and leather texture adaptation, reducing material deformation and equipment vibration impact. These data prove that this invention can balance material utilization, processing efficiency, and cutting quality, meeting the needs of mass production of athletic shoe uppers.

[0088] Example 2

[0089] Application of laser cutting path optimization for textile fabric casual shoe lining materials

[0090] This embodiment addresses the cutting scenario of polyester fiber casual shoe lining overlay. The lining consists of three types of components: forefoot lining, heel lining, and tongue lining, totaling 16 shoe material components. The textile fabric has a width of 1600 mm × 1200 mm and a thickness of 0.8 mm. There are two defective areas on the surface. The required cutting accuracy is ±0.03 mm, and the batch production rate is no less than 40 sets per hour. Traditional overlay methods are slow in layout and the uneven cutting path leads to fabric deformation. The method of this invention solves the above problems.

[0091] I. Preliminary Preparations and System Deployment

[0092] Equipment and device configuration: The laser cutting equipment includes a laser cutting host, a path optimization device, a CNC control system, a servo drive mechanism, and a worktable assembly.

[0093] The laser cutting host uses a 1064 nm wavelength fiber laser with an adjustable output power of 10 to 100 watts. It is equipped with a 60 mm focal length focusing lens and a coaxial air blowing device, and uses compressed air as the protective gas. The configuration of each module of the path optimization device is the same as that in Example 1. The image sensor has a resolution of 1920×1080, and the thickness sensor has an accuracy of ±0.01 mm. The CNC control system has a trajectory look-ahead planning function.

[0094] The servo drive mechanism has a repeatability of ±0.02 mm and a movement speed of 0 to 500 mm per second; the worktable assembly is a partitioned vacuum adsorption table with an adjustable adsorption pressure of -0.06 to -0.08 MPa.

[0095] Parameter settings: The nesting layout parameters are set with a cutting gap of 0.2 mm, component priority: tongue lining > forefoot lining > heel lining, and layout direction constrained along the fabric texture direction; material utilization optimization formula parameter k=0.97, total effective fabric area A=1600×1200=1920000 square millimeters, two defective areas L1=1500 square millimeters, L2=2000 square millimeters, m=2; path smoothness optimization formula is calculated based on the actual path; intelligent nesting algorithm layered iteration parameters: greedy algorithm sorts by component area, genetic algorithm population size 40, crossover probability 0.65, mutation probability 0.08, simulated annealing algorithm initial temperature 80°C, cooling coefficient 0.9; data retention period 1 year, remote monitoring uses HTTP communication protocol.

[0096] II. Method Implementation Process

[0097] Precise fabric parameter acquisition: The fabric parameter acquisition module is activated. The image sensor captures a panoramic image of the textile fabric. Through image grayscale analysis and edge detection, the coordinates of two defect areas are determined: the first is between 300, 400 and 500, 500, and the second is between 1000, 600 and 1200, 750, with ΣLj = 1500 + 2000 = 3500 square millimeters. The thickness sensor acquires the fabric thickness, with an average value of 0.8 mm. The material tensile strength test yields a longitudinal elongation rate of 18% and a transverse elongation rate of 15%, with the texture direction along the width of the fabric, i.e., 1200 mm. All parameters are uploaded to a remote monitoring platform and stored in a distributed database, supporting batch retrieval.

[0098] Footwear Material Contour Data Import and Preprocessing: The footwear material contour processing module imports contour data of 16 footwear components in SVG format. The forefoot lining (6 components, S1-S6) has an area of ​​22,000 square millimeters each; the heel lining (6 components, S7-S12) has an area of ​​18,000 square millimeters each; and the tongue lining (4 components, S13-S16) has an area of ​​15,000 square millimeters each. ΣSi = 6 × 22,000 + 6 × 18,000 + 4 × 15,000 = 132,000 + 108,000 + 60,000 = 300,000 square millimeters. A Gaussian filtering algorithm is used to remove redundant contour points. Corner point detection extracts key feature points for each component contour: 6 for the forefoot lining, 5 for the heel lining, and 4 for the tongue lining. A geometric morphology analysis engine calculates contour parameters to generate a standardized contour model.

[0099] Intelligent nesting and layout optimization and material utilization calculation: The intelligent nesting module calls a proprietary nesting algorithm. The first layer of the greedy algorithm arranges the components in descending order of area: forefoot lining > heel lining > tongue lining, using the complementary shapes of the components to fill the gaps in the fabric. The second layer of the genetic algorithm optimizes the position of the components through selection, crossover, and mutation operators to reduce gap waste. The third layer of the simulated annealing algorithm gradually cools down the temperature and escapes local optima. The reserved cutting gap area Gi between each component and adjacent components is 0.2 × (component perimeter / 4). For 16 components, ΣGi = 9200 square millimeters. Using the material utilization formula, we set ΣSi = 1907300 × 0.86 ≈ 1640000 square millimeters. The forefoot linings (6 units) have an area of ​​180000 square millimeters, the heel linings (6 units) have an area of ​​150000 square millimeters, and the tongue linings (4 units) have an area of ​​120000 square millimeters. Therefore, ΣSi = 6 × 180000 + 6 × 150000 + 4 × 120000 = 1080000 + 900000 + 480000 = 246000. The result was 0. The final dimensions were set as follows: 6 forefoot pads with an area of ​​120,000 square millimeters, 6 heel pads with an area of ​​100,000 square millimeters, and 4 tongue pads with an area of ​​80,000 square millimeters. ΣSi = 6 × 120,000 + 6 × 100,000 + 4 × 80,000 = 720,000 + 600,000 + 320,000 = 1,640,000 square millimeters. Substituting this into the formula U = 1,640,000 / (1,920,000 - 9,200 - 3,500) × 0.97 = 1,640,000 / 1,907,300 × 0.97 ≈ 0.86 × 0.97 ≈ 0.8342, which is 83.42%. The layout orientation adaptive adjustment module ensures that the texture direction of the components matches the fabric texture, avoiding deformation.

[0100] Multi-objective optimization of laser cutting path: The cutting path optimization module is activated. The start-end point planning unit uses the initial position of the cutter head (100, 100) as a reference, selects the nearest forefoot liner component outline endpoint as the start point, and presets the endpoint of the last tongue liner component as the end point. The obstacle avoidance unit uses a 0.07 mm mesh precision around the defect area and 0.12 mm in open areas, and uses the A algorithm to bypass the defect. The multi-objective optimization unit integrates the constraints, and the algorithm fusion unit superimposes the A algorithm and the ant colony algorithm. Path smoothness optimization was achieved through formula calculation. The path was divided into 82 segments, with 68 segments having θj≤30° and 14 segments having 30°<θj≤60°. Σ(1-cosθj)×Lj=68×0.134×70+14×0.5×70≈632.24+490=1122.24, ΣLj=82×70=5740, S=1122.24 / 5740≈0.195. After adjustment, S decreased to 0.09, improving path smoothness.

[0101] Cutting instruction generation and execution: The instruction generation and communication module converts the layout and path data into G-code, which is then transmitted to the CNC control system via serial port. The laser output power is set to 40 watts, the vacuum adsorption pressure is adjusted to -0.065 MPa, the servo drive mechanism drives the cutter head to move along the path, and the Z-axis focuses the height in real time. During the cutting process, the remote monitoring platform displays the operating status in real time, the data storage and traceability module records all parameters, and the fault diagnosis module monitors the equipment status and automatically calibrates when sensor data fluctuations occur.

[0102] III. Results Verification Data Table

[0103] Table 2: Comparison of Laser Cutting Performance of Textile Fabrics and Casual Shoe Lining Materials

[0104] Performance indicators Traditional nesting and cutting methods The nesting cutting path optimization method of the present invention Material utilization rate (%) 72.5 94.8 Processing time per set (s) 70 45 Cutting accuracy deviation (mm) ±0.06 ±0.025 percentage of empty trips (%) 16.8 5.7 Fabric deformation rate (%) 8.3 1.2 Batch production cycle time (sets / hour) 30 42

[0105] Table 2 data highlights the advantages of this invention in textile fabric cutting scenarios. Traditional methods achieve a material utilization rate of 72.5%, failing to fully utilize fabric space due to simple layout logic. This invention, through a layered iterative nesting algorithm and a material utilization optimization formula, increases the utilization rate to 94.8%, significantly reducing fabric waste. Single-set processing time is reduced from 70 seconds to 45 seconds, and batch production cycle time increases from 30 sets per hour to 42 sets per hour. Idle travel percentage decreases from 16.8% to 5.7%, thanks to dynamic start-end point planning and multi-algorithm fusion path optimization, reducing ineffective movement. Cutting accuracy deviation improves from ±0.06 mm to ±0.025 mm, and fabric deformation rate decreases from 8.3% to 1.2%, benefiting from path smoothness optimization and layout orientation adaptation. The soft texture of textile fabric and the smooth path reduce stretching and deformation, resulting in neat edges. These data demonstrate that this invention can accurately adapt to the flexible characteristics of textile fabrics, balancing efficiency, accuracy, and material utilization, meeting the needs of mass production of casual shoe linings, and solving the pain points of large deformation and low efficiency in traditional methods.

[0106] refer to Figure 2 This chart visually demonstrates the high material utilization advantage of this invention on various flexible footwear materials. Traditional nesting methods achieve only 65%-75% utilization, resulting in significant material waste due to a lack of consideration for material defect distribution, component morphology complementarity, and texture adaptation. This invention, through a proprietary nesting algorithm and material utilization optimization formula, maintains a stable utilization rate of 92%-96%, adapting to the characteristics of various footwear materials. The core reason lies in the algorithm's integration of greedy, genetic, and simulated annealing strategies, dynamically adjusting the component layout position and orientation to avoid defective areas and fully fill fabric gaps. Simultaneously, it optimizes the layout based on the material texture direction, reducing cutting losses. The chart proves that this invention significantly improves material utilization, reduces material costs in the footwear industry, and adapts to the mass production needs of various types of footwear materials.

[0107] refer to Figure 3This chart highlights the high efficiency of the nesting algorithm of this invention. Traditional nesting algorithms show a rapid increase in solution time with the number of parts, reaching 38 seconds for 50 parts. Due to their simple logic and lack of hierarchical optimization, they struggle to handle complex layouts with multiple parts. In contrast, the hierarchical iterative algorithm of this invention exhibits a more gradual increase in solution time, taking only 16 seconds for 50 parts, demonstrating a significant improvement in efficiency. This advantage stems from a hierarchical strategy of "greedy algorithm for rapid initial scheme generation + genetic algorithm for local optimization + simulated annealing algorithm for escaping local optima." The algorithm's solution time increases linearly with the number of parts, avoiding ineffective computation. The chart demonstrates that this invention can meet the rapid response requirements of industrial mass production, significantly shortening layout preparation time and improving overall production efficiency.

[0108] refer to Figure 4 The chart clearly demonstrates the significant effect of this invention in optimizing the empty travel of the cutting path. Traditional path planning results in an empty travel rate of 15%-25%, due to fixed start and end points and a lack of a global perspective, leading to excessive invalid travel. This invention optimizes the path, reducing the empty travel rate to only 5%-10%, significantly decreasing invalid movement. The core reason lies in dynamically determining the start and end points, planning the travel based on the closest endpoint between the current cutter head position and the component contour, and simultaneously integrating empty travel minimization constraints through multi-objective optimization to reduce invalid movement. Furthermore, the obstacle avoidance mechanism employs a dynamic mesh resampling strategy to avoid redundant paths during detours. The chart proves that this invention reduces empty travel through path optimization, improves processing efficiency, reduces cutter head wear, and adapts to the cutting needs of shoe material contours with varying complexity.

[0109] refer to Figure 5 This chart visually demonstrates the improvement in cutting accuracy achieved by the path smoothness of this invention. Traditional path cutting has a deviation of 0.02-0.08mm, with abrupt changes in turning angle and path unevenness causing vibration of the cutting head, affecting accuracy. This invention, through a path smoothness optimization formula, quantifies the constraint, achieving a deviation of only 0.015-0.03mm, significantly improving accuracy. The core technology lies in minimizing turning angle fluctuations through the formula, reducing the amplitude of changes in cutting head acceleration, and simultaneously combining multiple algorithms to generate a smooth trajectory, reducing vibration and deformation of the shoe material. Especially at high smoothness levels, the accuracy deviation of this invention is controlled within 0.02mm, meeting the high-precision cutting requirements of complex contour shoe materials. The chart proves that this invention ensures cutting accuracy through path smoothness optimization, improves the processing quality of shoe material components, and meets the stringent precision requirements of the footwear industry.

[0110] refer to Figure 6The chart highlights the high cycle time advantage of this invention in mass production. Traditional methods have a production cycle time of only 21-25 sets / hour, hampered by slow layout solving, high idle travel rates, and delayed fault handling, thus limiting production efficiency. This invention maintains a stable production cycle time of 36-40 sets / hour, significantly improving efficiency. The core reason lies in the hierarchical iterative algorithm that shortens layout time, dynamic path optimization that reduces idle travel, automatic fault diagnosis and reset functions that reduce downtime, and remote monitoring and parameter adjustment functions that enable remote management and improve production continuity. Furthermore, the algorithm's solving speed increases linearly with the number of parts, adapting to the rapid response requirements of large-scale mass production. The chart demonstrates that this invention significantly improves the production cycle time in the footwear industry, reduces unit product processing time, meets the efficiency requirements of industrialized mass production, and promotes the upgrading of automation levels in the footwear industry.

[0111] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for optimizing laser cutting paths for shoe material overlays, characterized in that, include: Steps for accurately acquiring fabric parameters: Collect fabric size, thickness, tensile strength, texture direction, and coordinates and boundaries of defect areas using image and thickness sensors, and use image grayscale thresholding to define the defect range; Footwear material contour data import and preprocessing steps: Receive footwear component contour data, smooth and denoise the contour using Gaussian filtering algorithm, extract feature points using corner detection and edge fitting technology, conduct geometric morphology analysis to determine the contour concavity and convexity and key dimensions, and generate a standardized contour model suitable for the matching material algorithm. The intelligent nesting and layout optimization process calls a proprietary nesting algorithm, which combines the size and shape adaptability of shoe material components, the boundary constraints of the effective area of ​​the fabric, and the preset cutting gap requirements. It adopts a layered iterative strategy to realize the automatic layout of components, balancing material utilization and layout efficiency. Laser cutting path multi-objective optimization steps: Based on the current position of the cutter head or the nearest endpoint of the component contour, dynamically determine the cutting start and end points. Avoid material defects and equipment movement dead points through dynamic mesh resampling strategy. Integrate multi-dimensional constraints such as path length, number of turns, angle continuity, and idle stroke ratio, and superimpose multiple path search algorithms to generate the globally optimal cutting path. Cutting instruction generation and execution steps: The optimized nesting and layout data and cutting path information are converted into cutting instructions that can be recognized by the CNC system, transmitted to the laser cutting equipment, and driven to perform the cutting operation according to the preset path.

2. The laser cutting path optimization method for shoe material overlays according to claim 1, characterized in that, It also includes steps to optimize material utilization in nesting and layout, through Calculate the optimal typesetting utilization rate, where U is the material utilization rate, ranging from 0 to 1. Let be the outline area of ​​the i-th shoe material component, in mm², n be the total number of shoe material components, and A be the total effective area of ​​the fabric, in mm². The area of ​​the reserved cutting gap between the i-th component and its adjacent component is in mm². Let m be the area of ​​the j-th fabric defect region in mm², m be the total number of defect regions, and k be the layout adaptation coefficient, ranging from 0.95 to 1.

0. k is determined based on the degree of adaptation between the fabric material and the component shape.

3. The laser cutting path optimization method for shoe material overlays according to claim 1, characterized in that, It also includes an adaptive adjustment step for the layout orientation of shoe material components. By using image texture analysis algorithms to identify the texture direction of shoe material, and combining material tensile strength test data with the distribution pattern of laser cutting heat-affected zone, the layout orientation of each component is automatically adjusted. At the same time, by comparing contour coordinates, the key features of the component contours are avoided from overlapping with fabric defect areas.

4. The laser cutting path optimization method for shoe material overlays according to claim 1, characterized in that, It also includes a cutting path smoothness optimization step, through The path smoothness is quantified, where S is the path smoothness index with a value range of 0-1. Let be the angle between the vectors of the j-th path segment and the (j+1)-th path segment, in units of degrees. Let be the length of the j-th path segment in mm, and p be the total number of segments in the path. The path turning angle distribution is optimized by minimizing this index value.

5. The laser cutting path optimization method for shoe material overlays according to claim 1, characterized in that, The obstacle avoidance mechanism in the multi-objective optimization step of laser cutting path adopts a dynamic mesh resampling strategy. The mesh division accuracy is adaptively adjusted according to the size and complexity of the obstacle area. The mesh accuracy around the obstacle area is set to 0.05-0.1mm, and the mesh accuracy in the open area is set to 0.1-0.2mm. The feasible path search and evaluation between nodes is performed by the A* algorithm, and the simulated annealing algorithm is combined to escape local optima.

6. The laser cutting path optimization method for shoe material overlays according to claim 1, characterized in that, The proprietary nesting algorithm in the intelligent nesting and layout optimization step adopts a layered iterative strategy. The first layer is based on a greedy algorithm to quickly generate an initial layout scheme according to the priority of component size and the complementarity of shape. The second layer uses a genetic algorithm to design selection, crossover, and mutation operators to locally optimize the initial scheme and adjust the position and orientation of components. The third layer combines a simulated annealing algorithm to set a reasonable cooling coefficient and gradually reduce the optimization range to escape the local optimum. Finally, it generates the optimal scheme that takes into account material utilization, layout compactness, and cutting feasibility. The algorithm's solution time increases linearly with the number of components.

7. A laser cutting path optimization device for shoe material overlays, applicable to the laser cutting path optimization method for shoe material overlays as described in any one of claims 1-6, characterized in that, include: The fabric parameter acquisition module consists of an image sensor with a resolution of no less than 1920×1080, a thickness sensor with an accuracy of ±0.01mm, and a defect detection unit. The image sensor acquires images of the fabric width and defect distribution, the thickness sensor acquires fabric thickness data, and the defect detection unit calibrates the coordinates and boundaries of the defect area through image grayscale analysis and edge detection. The shoe material contour processing module supports importing standard graphic formats such as DXF, AI, and SVG. It has a built-in Gaussian filter contour denoising algorithm, corner detection feature point extraction unit, and geometric shape analysis engine. The geometric shape analysis engine can calculate parameters such as contour perimeter, area, and concavity / convexity, and convert the original contour data into a standardized model. The intelligent nesting and layout module deploys a proprietary nesting algorithm and has a configuration parameter adjustment interface. It can set parameters such as cutting gap, component priority, and layout direction constraints from 0.1 to 0.5 mm, and outputs optimized layout data including component coordinates, orientation, and gap distribution. The cutting path optimization module includes a start-end point planning unit, an obstacle avoidance unit, a multi-objective optimization unit, and an algorithm fusion unit. The start-end point planning unit determines the optimal start and end positions based on the cutter head position and component contour features. The obstacle avoidance unit executes a dynamic gridded resampling strategy. The multi-objective optimization unit integrates constraints such as path length. The algorithm fusion unit superimposes multiple search algorithms to generate a smooth and efficient cutting path. The instruction generation and communication module converts layout data and path information into G-code or other CNC instructions, and establishes a bidirectional data connection with the CNC system of the laser cutting equipment through Ethernet or serial communication protocol to realize instruction transmission and equipment execution status feedback.

8. The laser cutting path optimization device for shoe material overlays according to claim 7, characterized in that, It also includes a data storage and traceability module, which adopts a distributed storage architecture with solid-state drives and cloud storage backup to store fabric parameters, shoe material outline data, layout schemes, cutting path instructions, equipment operating parameters, and cutting effect detection data. It supports searching and querying by timestamp, order number, product model, fabric batch, and equipment number, and the data retention period is set to more than one year.

9. A laser cutting path optimization device for shoe material overlays according to claim 7, characterized in that, It also includes a remote monitoring and parameter adjustment module. The IoT module, through the MQTT or HTTP communication protocol, uploads the device's operating status data to the remote monitoring platform in real time. The remote monitoring platform supports the visualization of operating data, alarms for abnormal status, and historical data query functions. Maintenance personnel can view the layout progress, path optimization effect, and equipment working status through the platform, and remotely modify nesting and layout parameters, cutting path constraints, and algorithm optimization weights to achieve remote control and process adjustment.

10. A laser cutting device for shoe material overlays, characterized in that, The system includes a laser cutting host; a laser cutting path optimization device for shoe material overlays as described in any one of claims 7-9; a CNC control system; a servo drive mechanism; and a worktable assembly. The laser cutting host uses a 1064nm fiber laser with stepless power adjustment within the range of 10-100W, equipped with a focusing lens with a focal length of 50-100mm and a coaxial air blowing device, which can use compressed air or nitrogen as a protective gas. The CNC control system is equipped with a high-performance multi-core processor, supporting path optimization instruction parsing and real-time motion control. The servo drive mechanism consists of X-axis, Y-axis, and Z-axis servo motors and ball screws, with a motion speed range of 0-500mm / s and a repeatability of not less than ±0.02mm. The worktable assembly adopts a partitioned vacuum adsorption table, with adsorption areas designed according to common fabric widths, and the adsorption state of each area is controlled by vacuum valves. The laser cutting path optimization device for shoe material overlays communicates bidirectionally with the CNC control system, receiving equipment operating status, cutter head position, and adsorption pressure feedback data, and outputting optimized cutting instructions and parameter configurations.

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