An intelligent path planning method and system for adaptive cloth cutting

Abstract

The present application relates to the technical field of image recognition, in particular to an intelligent path planning method and system for self-adaptive cloth cutting, comprising obtaining physical parameters and image data of multi-layer cloth, establishing a cloth interlayer friction model; calculating stress component data and stress transmission efficiency based on the cloth interlayer friction model, predicting interlayer relative displacement data of the multi-layer cloth in the processing process; according to the interlayer relative displacement data, analyzing the stress distribution state of the cloth in the processing process, and obtaining the redistribution of the processing points; based on the redistributed processing points, a spiral processing path advancing layer by layer from the edge to the center of the cloth is generated, and the stress difference between adjacent processing points in the spiral processing path is controlled within a preset threshold; in the processing process, when it is detected that the interlayer relative displacement data exceeds the preset threshold, the displacement vector is decomposed into components along the processing direction and perpendicular to the processing direction, and the subsequent processing path is translated and rotated to compensate according to the component size.

Description

Technical Field

[0001] This invention relates to the field of image recognition technology, specifically to an intelligent path planning method and system for adaptive fabric cutting. Background Technology

[0002] Most automatic cutting equipment on the market currently uses a preset fixed path control method, lacking the ability to dynamically adjust according to the characteristics of the fabric, such as material, thickness, and texture direction. This rigid path planning method is difficult to cope with the differentiated cutting needs of different types of fabrics, and can easily lead to problems such as uneven processing quality, edge burrs, and strand deformation.

[0003] Existing path planning algorithms are mainly based on geometric optimization principles, focusing on static optimization of the shortest path or fewest turns, and lack a real-time perception and response mechanism for changes in the physical properties of the fabric during processing. When the fabric has local wrinkles, uneven tension, or positional shifts, traditional systems cannot adjust the cutting parameters and path trajectory in time, resulting in decreased cutting accuracy and increased scrap rate.

[0004] Therefore, an intelligent cutting method is needed that can adaptively adjust the cutting path and optimize control parameters in real time based on fabric characteristics to improve cutting quality. To this end, an intelligent path planning method and system for adaptive fabric cutting is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent path planning method and system for adaptive fabric cutting, which determines the cutting path planning by analyzing and updating the cutting points.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] An intelligent path planning method and system for adaptive fabric cutting includes:

[0008] Obtain the physical parameters and image data of multi-layer fabrics and establish a friction model between fabric layers;

[0009] Based on the interlayer friction model of the fabric, the stress component data of the local stress field generated by the processing tool on the surrounding fabric are calculated, and the stress transfer efficiency of the stress release of the completed area to the untreated area is obtained, so as to predict the interlayer relative displacement data of the multilayer fabric during the processing.

[0010] Based on the interlayer relative displacement data, the stress distribution of the fabric during the processing is analyzed, high stress concentration areas and low stress areas are identified, intermediate treatment points are added in high stress concentration areas, and the density of treatment points is reduced in low stress areas to obtain the redistribution of treatment points.

[0011] Based on the redistributed processing points, a spiral processing path is generated that advances layer by layer from the edge of the fabric to the center. The stress difference between adjacent processing points in the spiral processing path is controlled within a preset threshold.

[0012] During the processing, when the interlayer relative displacement data exceeds the preset threshold, the displacement vector is decomposed into components along the processing direction and perpendicular to the processing direction, and the subsequent processing path is translated and rotated to compensate according to the component magnitude.

[0013] Preferably, the establishment of the interlayer friction model of the fabric includes: acquiring material density data, fiber orientation data, and surface roughness data between each two adjacent layers in the multilayer fabric; calculating the elastic modulus parameter and Poisson's ratio parameter of each layer of fabric based on the material density data and fiber orientation data; and using image recognition technology to analyze the surface roughness data to obtain the interlayer contact area distribution data and contact pressure distribution data.

[0014] Based on the elastic modulus parameter, Poisson's ratio parameter, contact area distribution data, and contact pressure distribution data, an interlayer friction relationship model describing the static friction coefficient and dynamic friction coefficient between each layer of fabric is established.

[0015] Preferably, the specific process for acquiring the stress component data includes: acquiring the geometric shape parameters, processing speed parameters, and processing depth parameters of the processing tool; determining the contact boundary range with the fabric based on the geometric shape parameters; calculating the normal pressure distribution and tangential shear force distribution applied to the fabric according to the processing speed parameters and processing depth parameters; calculating the stress attenuation coefficient of the fabric within different radii of the contact point; and acquiring the stress component data of each point on the fabric within a preset range around the tool by combining the normal pressure distribution, tangential shear force distribution, and stress attenuation coefficient.

[0016] Preferably, the specific process for obtaining the stress transfer efficiency includes: for the treated area, analyzing the stress relaxation effect after material removal, calculating the degree of influence of stress redistribution on the adjacent untreated area; using the stress release amount of the treated area as a new boundary condition, calculating the disturbance to the stress state of the untreated area, and obtaining the stress transfer efficiency.

[0017] Preferably, the process of acquiring the interlayer relative displacement data includes:

[0018] The multi-layer fabric is divided into regular grid cells, and the initial coordinate data of each grid cell is obtained. Based on the stress component data and stress transfer efficiency, the resultant force vector of each grid cell is calculated. Combining the static friction coefficient and dynamic friction coefficient, it is determined whether interlayer slippage occurs in each grid cell. For grid cells that have slipped, the displacement increment is calculated based on the force vector and the elastic parameters of the fabric. The displacement increment is accumulated to the coordinate data of the corresponding grid cell to obtain the updated coordinate data. By comparing the coordinate differences of corresponding grid cells in adjacent fabric layers, the interlayer relative displacement data is obtained. The interlayer relative displacement data includes the displacement direction vector and the displacement amplitude.

[0019] Preferably, generating a spiral shrinkage processing path includes: determining the outer contour boundary of the fabric and using the outer contour as the starting layer of the spiral path; setting the interlayer spacing parameters and angle step parameters of the spiral path; starting from the outer contour boundary, moving counterclockwise according to the preset angle step parameters, shrinking inward by one interlayer spacing for each angle step; and detecting the distance between the current path point and the redistributed processing points in real time during the path generation process.

[0020] When the distance between a path point and the nearest processing point is less than a preset threshold, the processing point is added to the spiral path as a path node; the spiral contraction continues until the geometric center of the fabric is reached, forming a complete spiral contraction processing path; the stress difference of the generated spiral path is verified to ensure that the stress difference between adjacent path nodes is within the preset threshold range.

[0021] Preferably, the translation and rotation compensation for subsequent processing paths based on component magnitude includes: real-time monitoring of displacement vector data at various points of the fabric during processing; when the magnitude of the displacement vector exceeds a preset threshold, extracting the projection component of the displacement vector in the current processing direction and the projection component perpendicular to the processing direction; performing translation compensation along the processing direction for subsequent processing path nodes based on the magnitude and direction of the projection component in the processing direction; calculating the rotation angle deviation of the fabric based on the magnitude and direction of the projection component perpendicular to the processing direction; and performing rotation transformation compensation for subsequent processing path nodes based on the rotation angle deviation.

[0022] Translation and rotation compensation are applied to the remaining unprocessed path nodes to obtain the compensated processed path.

[0023] An intelligent path planning system for adaptive fabric cutting includes: a model building module for acquiring physical parameters and image data of multi-layer fabric and establishing a friction model between fabric layers;

[0024] The interlayer data acquisition module is used to calculate stress component data and stress transfer efficiency based on the interlayer friction model of the fabric, and to predict the interlayer relative displacement data of multilayer fabrics during the processing.

[0025] The processing point update module is used to analyze the stress distribution of the fabric during the processing based on the interlayer relative displacement data, identify high stress concentration areas and low stress areas, and obtain the redistribution of processing points.

[0026] The processing path acquisition module is used to generate a spiral shrinkage processing path that advances layer by layer from the edge of the fabric to the center based on the redistributed processing points.

[0027] The processing path update module is used to perform translation and rotation compensation on the subsequent processing path according to the component magnitude when the interlayer relative displacement data is detected to exceed the preset threshold during the processing.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0029] 1. This invention monitors the displacement vector data of various points on the fabric in real time. When the detected displacement exceeds a threshold, the displacement vector is decomposed into two components: the processing direction and the vertical direction, thereby achieving translational and rotational compensation of the path. This allows the cutter to adaptively adjust according to the actual dynamic changes of the fabric, maintaining a high degree of consistency between the cutting path and the fabric state without pausing or manual correction. This reduces the impact of accumulated errors on the accuracy of the finished product, ensuring high consistency between the cutting contour and the design drawing. Furthermore, the compensation strategy of this invention is superior to traditional fixed compensation methods in terms of real-time performance and flexibility. It can handle the nonlinear deformation problem of the fabric under complex working conditions, demonstrating a high degree of intelligence and robustness.

[0030] 2. This invention establishes a friction model between fabric layers, comprehensively considering physical parameters such as material density, fiber direction, and surface roughness, and combines image recognition to obtain the contact area distribution and contact pressure distribution, describing the frictional relationship between multiple layers of fabric. It can accurately predict the relative displacement trend between fabric layers during the cutting process, thereby achieving precise modeling of the fabric's stress state. By calculating the local stress distribution and stress transfer efficiency before path planning, this invention can pre-identify high stress concentration areas and potential slippage risk points, thus avoiding common problems such as layer shift, edge warping, and uneven stress during the cutting process at the path generation stage. It reduces cutting deviations and rework caused by instability between fabric layers, and improves the stability of multi-layer fabrics in continuous processing scenarios.

[0031] 3. A spiral shrinkage path is generated, advancing layer by layer from the edge of the fabric towards the center. By setting a stress difference threshold, the stress change between adjacent processing points is ensured to be within a controllable range. This not only evenly distributes the cutting stress and avoids excessive stress concentration in local areas, but also allows the fabric to gradually shrink and stabilize during the overall processing, thereby effectively reducing the stress accumulation effect. This ensures that the overall geometry remains unchanged when cutting complex patterns, reducing stress abrupt changes and crack propagation problems caused by unreasonable path design. Attached Figure Description

[0032] Figure 1 A flowchart of an intelligent path planning method for adaptive fabric cutting provided by the present invention;

[0033] Figure 2 A structural diagram of an intelligent path planning system for adaptive fabric cutting provided by the present invention;

[0034] Figure 3 This is a schematic diagram of the path update process provided by the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.

[0036] Example 1:

[0037] Please see Figure 1 and Figure 2 This invention provides an intelligent path planning method for adaptive fabric cutting, applied to an intelligent path planning system for adaptive fabric cutting. The technical solution is as follows:

[0038] Obtain the physical parameters and image data of multi-layer fabrics and establish a friction model between fabric layers;

[0039] The physical parameters include fabric thickness data, weight data, tensile strength data, and elastic recovery rate data;

[0040] The thickness data is obtained by using a contact thickness gauge to measure multiple points evenly distributed on the fabric surface. The weight data is obtained by weighing the fabric per unit area using a precision electronic scale. The tensile strength data and elastic recovery rate data are obtained by performing a standard tensile test on the fabric sample using a universal testing machine.

[0041] The image data includes fabric surface texture images acquired by a camera, surface elevation data obtained by laser scanning, and fiber orientation distribution images acquired by a microscope.

[0042] Furthermore, the establishment of the interlayer friction model of the fabric includes: acquiring material density data, fiber orientation data, and surface roughness data between each two adjacent layers in the multilayer fabric; calculating the elastic modulus parameter and Poisson's ratio parameter of each layer of fabric based on the material density data and fiber orientation data; and using image recognition technology to analyze the surface roughness data to obtain the interlayer contact area distribution data and contact pressure distribution data.

[0043] Based on the elastic modulus parameter, Poisson's ratio parameter, contact area distribution data, and contact pressure distribution data, an interlayer friction relationship model describing the static friction coefficient and dynamic friction coefficient between each layer of fabric is established.

[0044] Specifically, the material density data is calculated by dividing the weight data by the thickness data; the fiber orientation data acquisition process includes: grayscale processing of the fiber orientation distribution image obtained by the microscope, identification of fiber boundaries using the Sobel edge detection operator, extraction of the main orientation angles of the fibers using Hough transform, and statistical analysis of the fiber distribution density within each angle interval using the orientation gradient histogram to obtain the probability distribution data of the fiber orientation; the surface roughness data acquisition process includes: high-frequency filtering of the surface elevation data obtained by laser scanning, calculation of the height difference between adjacent measurement points, and statistical analysis using the arithmetic mean roughness Ra and root mean square roughness Rq as characterization parameters to obtain the roughness distribution data of the fabric surface.

[0045] The process for obtaining the elastic modulus parameter includes: extracting the linear portion of the elastic deformation stage from the obtained stress-strain curve, and calculating the ratio of stress to strain to obtain the elastic modulus E. Considering the influence of fiber direction, the elastic moduli E1 and E2 along the fiber direction and perpendicular to the fiber direction are calculated respectively. The process for obtaining the Poisson's ratio parameter includes: during the tensile test, simultaneously measuring the longitudinal strain and transverse strain using strain gauges, and calculating the ratio of transverse strain to longitudinal strain to obtain the Poisson's ratio.

[0046] The process of acquiring contact area distribution data includes: based on surface texture images, using a threshold segmentation method to identify contact peaks and valleys, calculating the proportion of contact peaks in the total surface area, and establishing a model of the relationship between contact area and normal load. The process of acquiring contact pressure distribution data includes: based on contact mechanics theory, combining surface roughness data and elastic modulus parameters, using the Greenwood-Williamson contact model to calculate the contact pressure of the micro-protrusions, obtaining the pressure distribution function across the entire contact surface through integral calculations, and establishing a mathematical relationship between contact pressure and contact area and normal load.

[0047] In this embodiment, by collecting physical parameters such as fabric thickness, weight, tensile strength, and elastic recovery rate, and fusing texture images, elevation data, and fiber orientation information, a precise interlayer friction model of multi-layer fabric is established. Combining data such as material density, fiber direction, and surface roughness, the elastic modulus and Poisson's ratio are calculated, and the contact area and pressure distribution are obtained, achieving a quantitative description of the static and dynamic friction coefficients. The overall effect is to comprehensively reveal the interlayer friction mechanism of the fabric, improve the accuracy of stress transmission and slip prediction, and provide reliable support for intelligent cutting and process optimization.

[0048] Based on the interlayer friction model of the fabric, the stress component data of the local stress field generated by the processing tool on the surrounding fabric are calculated, and the stress transfer efficiency of the stress release of the completed area to the untreated area is obtained, so as to predict the interlayer relative displacement data of the multilayer fabric during the processing.

[0049] Specifically, after establishing the interlayer friction relationship model, the stress component data acquisition process includes: discretizing the contact area between the tool and the fabric into a finite element mesh; determining the normal pressure and tangential shear force on each mesh element based on the tool's geometric and motion parameters; combining the static and dynamic friction coefficients in the interlayer friction relationship model to determine the friction state of each layer of fabric at that mesh element; and calculating the components of the friction force in each coordinate direction. The stress transfer efficiency acquisition process includes: establishing a stress transfer equation for the multi-layer fabric based on elasticity theory; using the stress on the upper layer of fabric as a boundary condition; obtaining the stress attenuation law between layers by solving a system of partial differential equations; and defining the stress transfer efficiency as the ratio of the stress amplitude of the lower layer of fabric to the stress amplitude of the upper layer of fabric.

[0050] Furthermore, the specific process for acquiring the stress component data includes: acquiring the geometric parameters, processing speed parameters, and processing depth parameters of the processing tool; determining the contact boundary range with the fabric based on the geometric parameters; calculating the normal pressure distribution and tangential shear force distribution applied to the fabric according to the processing speed parameters and processing depth parameters; calculating the stress attenuation coefficient of the fabric within different radii of the contact point; and acquiring the stress component data of each point on the fabric within a preset range around the tool by combining the normal pressure distribution, tangential shear force distribution, and stress attenuation coefficient.

[0051] Specifically, the process of obtaining the normal pressure distribution includes: determining the contact boundary between the tool and the fabric based on the tool's geometric parameters, and establishing a spatial distribution function of the contact pressure. For circular tools, the pressure distribution is calculated using Hertz contact theory; for rectangular tools, a rectangular load distribution model is used. Combined with the processing depth parameter, the normal pressure distribution throughout the contact area is obtained through integration. The process of obtaining the tangential shear force distribution includes: calculating the tangential velocity distribution of the tool relative to the fabric based on the processing speed parameter; combining the dynamic friction coefficient in the interlayer friction relationship model, calculating the tangential shear force at each point using Coulomb's friction law; and considering the tool edge effect, correcting the calculation of the shear force in the boundary region.

[0052] The process of obtaining the stress attenuation coefficient includes: establishing a polar coordinate system with the contact point as the origin; and, according to the stress concentration theory in elasticity, defining the stress attenuation coefficient as the ratio of the stress at the distance from the contact point to the stress at the contact point.

[0053] In this embodiment, the stress components of the fabric around the tool are accurately calculated using an interlayer friction relationship model combined with tool geometry, speed, and depth parameters. The stress transfer efficiency is solved using finite element method and elasticity mechanics to quantify the stress attenuation law between multiple layers of fabric. Pressure distribution, shear force distribution, and stress attenuation coefficient are introduced to achieve accurate prediction of interlayer relative displacement during the processing. The overall effect is to accurately reveal the local stress and interlayer displacement behavior of the fabric under the action of the tool, providing theoretical support and data basis for intelligent cutting path planning and process parameter optimization.

[0054] Furthermore, the specific process for obtaining the stress transfer efficiency includes: for the treated area, analyzing the stress relaxation effect after material removal, calculating the degree of influence of stress redistribution on the adjacent untreated area; using the stress release amount of the treated area as a new boundary condition, calculating the disturbance to the stress state of the untreated area, and obtaining the stress transfer efficiency.

[0055] Specifically, the process for obtaining the degree of stress redistribution influence includes: treating the treated area as a stress release source; calculating the stress redistribution using the principle of virtual work based on the changes in boundary conditions after material removal; and obtaining the stress change at each point in the adjacent untreated area through numerical integration. The process for obtaining the stress state disturbance includes: using the stress release amount in the treated area as a new boundary condition to establish a modified stress field equation set; meshing the untreated area using the finite difference method; and solving for the disturbed stress distribution based on the stress balance equation and strain compatibility equation. The stress transfer efficiency is defined as the ratio of the disturbed stress to the original stress. The average transfer efficiency of the region is obtained by statistically analyzing the stress transfer efficiency of all grid points in the untreated area.

[0056] In this embodiment, by analyzing the stress relaxation effect after the removal of material from the treated area, the disturbance of stress redistribution to the adjacent untreated area is calculated. Using the stress release amount as a new boundary condition, the corrected stress field is solved to obtain the regional average transmission efficiency. The overall effect is to comprehensively reveal the influence of stress release during processing on the surrounding fabric, quantify the spatial distribution characteristics of stress transmission efficiency, and achieve accurate assessment of the stress state of the untreated area, providing a reliable basis for optimizing the processing sequence and quality control of multi-layer fabrics.

[0057] Furthermore, the process of acquiring the inter-layer relative displacement data includes:

[0058] The multi-layer fabric is divided into regular grid cells, and the initial coordinate data of each grid cell is obtained. Based on the stress component data and stress transfer efficiency, the resultant force vector of each grid cell is calculated. Combining the static friction coefficient and dynamic friction coefficient, it is determined whether interlayer slippage occurs in each grid cell. For grid cells that have slipped, the displacement increment is calculated based on the force vector and the elastic parameters of the fabric. The displacement increment is accumulated to the coordinate data of the corresponding grid cell to obtain the updated coordinate data. By comparing the coordinate differences of corresponding grid cells in adjacent fabric layers, the interlayer relative displacement data is obtained. The interlayer relative displacement data includes the displacement direction vector and the displacement amplitude.

[0059] Specifically, the process of obtaining the resultant force vector includes: for each mesh cell, calculating the direct force acting on the cell based on the stress component data generated by the tool; calculating the indirect force from the stress release in the processed area based on the stress transfer efficiency; and calculating the friction between adjacent layers, considering the effect of interlayer friction. The resultant force vector of each mesh cell is the sum of the direct force, the indirect force, and the interlayer friction. The direct force is obtained by multiplying the stress by the cell area, the indirect force is obtained by multiplying the stress transfer efficiency by the released stress, and the interlayer friction is calculated based on the normal pressure and the friction coefficient.

[0060] In this embodiment, based on stress components and stress transfer efficiency, and considering direct forces, indirect forces generated by stress release, and interlayer friction, the resultant force vector of each grid element is accurately calculated. The displacement increment is then calculated using the fabric's elastic parameters, and the coordinate data is dynamically updated to obtain the direction and amplitude of the relative interlayer displacement. The overall effect is a comprehensive characterization of the slippage behavior of multi-layered fabric during processing, achieving high-precision prediction of interlayer displacement and providing robust data support for stability control of the cutting process and fabric deformation compensation.

[0061] Based on the interlayer relative displacement data, the stress distribution of the fabric during the processing is analyzed, high stress concentration areas and low stress areas are identified, and the redistribution of processing points is obtained.

[0062] Specifically, the stress distribution state acquisition process includes: calculating the stress tensor of each grid cell based on the resultant force vector data of each grid cell, and calculating the equivalent stress using the Von Mises stress criterion. A stress distribution cloud map is established to identify areas with large stress gradients. The high stress concentration area identification process includes: setting a stress threshold σ_threshold, defining areas with equivalent stress greater than this threshold as high stress concentration areas, and extracting each independent high stress area using connected component analysis. The low stress area identification process includes: defining areas with equivalent stress less than 0.3σ_threshold as low stress areas. The treatment point redistribution process includes: reducing the density of treatment points in high stress concentration areas, transferring existing treatment points to surrounding low stress areas to ensure the stress difference between adjacent treatment points, and adjusting the position of treatment points through an iterative optimization algorithm until the stress difference constraint condition is met.

[0063] Based on the redistributed processing points, a spiral processing path is generated that advances layer by layer from the edge of the fabric to the center. The stress difference between adjacent processing points in the spiral processing path is controlled within a preset threshold.

[0064] Furthermore, generating a spiral shrinkage processing path includes: determining the outer contour boundary of the fabric and using the outer contour as the starting layer of the spiral path; setting the interlayer spacing parameters and angle step parameters of the spiral path; starting from the outer contour boundary, moving counterclockwise according to the preset angle step parameters, shrinking inward by one interlayer spacing for each angle step; and detecting the distance between the current path point and the redistributed processing points in real time during the path generation process.

[0065] When the distance between a path point and the nearest processing point is less than a preset threshold, the processing point is added to the spiral path as a path node; the spiral contraction continues until the geometric center of the fabric is reached, forming a complete spiral contraction processing path; the stress difference of the generated spiral path is verified to ensure that the stress difference between adjacent path nodes is within the preset threshold range.

[0066] In this embodiment, a stress distribution cloud map is constructed based on interlayer relative displacement data. The Von Mises criterion is used to identify high-stress concentration and low-stress areas, and iterative optimization is employed to redistribute processing points, ensuring stress gradient balance. A spiral shrinkage path advancing from the fabric edge to the center is generated based on the processing point locations, and the stress difference between nodes is verified in real time to ensure stable stress control throughout the processing. The overall effect is a significant improvement in the uniformity and accuracy of fabric cutting, a reduction in the risk of slippage and deformation caused by high stress, and high-reliability support for intelligent path planning and process optimization.

[0067] During the processing, when the interlayer relative displacement data exceeds the preset threshold, the displacement vector is decomposed into components along the processing direction and perpendicular to the processing direction, and the subsequent processing path is translated and rotated to compensate according to the component magnitude.

[0068] Furthermore, the translation and rotation compensation for subsequent processing paths based on the component magnitudes includes: real-time monitoring of displacement vector data at various points on the fabric during processing; when the magnitude of the displacement vector exceeds a preset threshold, extracting the projection component of the displacement vector in the current processing direction and the projection component perpendicular to the processing direction; performing translation compensation along the processing direction for subsequent processing path nodes based on the magnitude and direction of the projection component in the processing direction; calculating the rotation angle deviation of the fabric based on the magnitude and direction of the projection component perpendicular to the processing direction; and performing rotation transformation compensation for subsequent processing path nodes based on the rotation angle deviation.

[0069] Translation and rotation compensation are applied to the remaining unprocessed path nodes to obtain the compensated processed path.

[0070] In this embodiment, when the interlayer relative displacement exceeds the threshold, the displacement vector is decomposed into components along the processing direction and the vertical direction. Real-time translation and rotation compensation is applied to the subsequent path to correct node positions and angles. The overall effect is to dynamically correct the fabric processing trajectory, ensuring that the tool path is synchronized with fabric deformation, significantly reducing cutting errors caused by displacement accumulation, improving the stability and accuracy of multi-layer fabric processing, and achieving efficient adaptation and control to complex deformations.

[0071] This invention collects physical parameters such as fabric thickness, weight, tensile strength, and elastic recovery rate, and integrates texture, elevation, and fiber orientation images to construct an interlayer friction model. It accurately quantifies static and dynamic friction coefficients, revealing the friction and stress transmission mechanism. Combining tool geometry, speed, depth, and stress attenuation coefficient, it calculates local stress components and stress transmission efficiency, predicts interlayer relative displacement, and calculates the resultant force vector by integrating direct force, indirect force, and frictional force. It dynamically updates the grid coordinates to obtain the displacement direction and amplitude. Based on the displacement data, it generates a stress distribution cloud map, identifies high and low stress areas, iteratively optimizes the processing point layout, and forms a stress-balanced spiral contraction path. If displacement exceeds a threshold during processing, the displacement vector is decomposed and translation and rotation compensation are implemented, real-time correction of the remaining path node positions and angles. The overall effect is to achieve integrated and accurate modeling of fabric friction, stress, displacement, and path control, comprehensively improving the stress uniformity and trajectory stability of multi-layer fabric cutting, significantly reducing slippage and deformation risks, ensuring processing accuracy and quality, and providing highly reliable theoretical and data support for intelligent path planning and process parameter optimization. The overall process refers to... Figure 3 .

[0072] Example 2:

[0073] This invention provides an intelligent path planning system for adaptive fabric cutting, the specific technical solution of which is as follows:

[0074] The model building module is used to acquire the physical parameters and image data of multi-layer fabrics and to build a friction model between the fabric layers.

[0075] The interlayer data acquisition module is used to calculate stress component data and stress transfer efficiency based on the interlayer friction model of the fabric, and to predict the interlayer relative displacement data of multilayer fabrics during the processing.

[0076] The processing point update module is used to analyze the stress distribution of the fabric during the processing based on the interlayer relative displacement data, identify high stress concentration areas and low stress areas, and obtain the redistribution of processing points.

[0077] The processing path acquisition module is used to generate a spiral shrinkage processing path that advances layer by layer from the edge of the fabric to the center based on the redistributed processing points.

[0078] The processing path update module is used to perform translation and rotation compensation on the subsequent processing path according to the component magnitude when the interlayer relative displacement data is detected to exceed the preset threshold during the processing.

[0079] Furthermore, the establishment of the interlayer friction model of the fabric includes: acquiring material density data, fiber orientation data, and surface roughness data between each two adjacent layers in the multilayer fabric; calculating the elastic modulus parameter and Poisson's ratio parameter of each layer of fabric based on the material density data and fiber orientation data; and using image recognition technology to analyze the surface roughness data to obtain the interlayer contact area distribution data and contact pressure distribution data.

[0080] Based on the elastic modulus parameter, Poisson's ratio parameter, contact area distribution data, and contact pressure distribution data, an interlayer friction relationship model describing the static friction coefficient and dynamic friction coefficient between each layer of fabric is established.

[0081] Furthermore, the specific process for acquiring the stress component data includes: acquiring the geometric parameters, processing speed parameters, and processing depth parameters of the processing tool; determining the contact boundary range with the fabric based on the geometric parameters; calculating the normal pressure distribution and tangential shear force distribution applied to the fabric according to the processing speed parameters and processing depth parameters; calculating the stress attenuation coefficient of the fabric within different radii of the contact point; and acquiring the stress component data of each point on the fabric within a preset range around the tool by combining the normal pressure distribution, tangential shear force distribution, and stress attenuation coefficient.

[0082] Furthermore, the specific process for obtaining the stress transfer efficiency includes: for the treated area, analyzing the stress relaxation effect after material removal, calculating the degree of influence of stress redistribution on the adjacent untreated area; using the stress release amount of the treated area as a new boundary condition, calculating the disturbance to the stress state of the untreated area, and obtaining the stress transfer efficiency.

[0083] Furthermore, the process of acquiring the inter-layer relative displacement data includes:

[0084] The multi-layer fabric is divided into regular grid cells, and the initial coordinate data of each grid cell is obtained. Based on the stress component data and stress transfer efficiency, the resultant force vector of each grid cell is calculated. Combining the static friction coefficient and dynamic friction coefficient, it is determined whether interlayer slippage occurs in each grid cell. For grid cells that have slipped, the displacement increment is calculated based on the force vector and the elastic parameters of the fabric. The displacement increment is accumulated to the coordinate data of the corresponding grid cell to obtain the updated coordinate data. By comparing the coordinate differences of corresponding grid cells in adjacent fabric layers, the interlayer relative displacement data is obtained. The interlayer relative displacement data includes the displacement direction vector and the displacement amplitude.

[0085] When generating a spiral processing path, the processing parameters are adjusted according to the material type involved in the current processing area: for softer material areas, the tool feed rate is reduced and the depth of cut is decreased to avoid material stretching and deformation; for harder material areas, the tool speed is appropriately increased and cooling measures are added to ensure cutting quality.

[0086] When the spiral path passes through the boundary area of ​​multiple materials, transition buffer zones are set before and after the boundary line. A progressive parameter adjustment strategy is implemented within the buffer zone: taking the boundary line as the dividing point, the parameters gradually transition from the optimal parameters of the current material to the optimal parameters of the target material within the previous buffer zone. The transition step length is determined according to the degree of difference in physical parameters between the two materials and the set transition smoothness to ensure that no processing defects caused by abrupt changes occur during parameter switching.

[0087] In this embodiment, the differentiated processing strategy effectively solves the problem of uneven quality in the processing of multi-material fabrics by intelligently identifying the characteristics of different materials and dynamically adjusting the processing parameters. In particular, the progressive parameter transition technology in the boundary area eliminates the processing defects caused by parameter abrupt changes in traditional methods, ensuring the processing continuity and stability at the material transition point. The overall solution improves the processing consistency of multi-material fabrics, reduces the scrap rate, and increases production efficiency.

[0088] Furthermore, generating a spiral shrinkage processing path includes: determining the outer contour boundary of the fabric and using the outer contour as the starting layer of the spiral path; setting the interlayer spacing parameters and angle step parameters of the spiral path; starting from the outer contour boundary, moving counterclockwise according to the preset angle step parameters, shrinking inward by one interlayer spacing for each angle step; and detecting the distance between the current path point and the redistributed processing points in real time during the path generation process.

[0089] When the distance between a path point and the nearest processing point is less than a preset threshold, the processing point is added to the spiral path as a path node; the spiral contraction continues until the geometric center of the fabric is reached, forming a complete spiral contraction processing path; the stress difference of the generated spiral path is verified to ensure that the stress difference between adjacent path nodes is within the preset threshold range.

[0090] Furthermore, the translation and rotation compensation for subsequent processing paths based on component magnitudes includes: real-time monitoring of displacement vector data at various points on the fabric during processing; when the magnitude of the displacement vector exceeds a preset threshold, extracting the projection component of the displacement vector in the current processing direction and the projection component perpendicular to the processing direction; performing translation compensation along the processing direction on subsequent processing path nodes based on the magnitude and direction of the projection component perpendicular to the processing direction; calculating the rotation angle deviation of the fabric based on the magnitude and direction of the projection component perpendicular to the processing direction; performing rotation transformation compensation on subsequent processing path nodes based on the rotation angle deviation; and applying the translation and rotation compensation to the remaining unprocessed path nodes to obtain the compensated processing path.

[0091] The translation and rotation compensation also includes adaptive damping control: establishing a dynamic response model for the fabric to analyze its vibration characteristics and damping coefficient during processing; when vibration or swaying of the fabric is detected, active damping control is achieved by adjusting the processing speed and tool pressure; setting multi-level compensation thresholds and adopting different intensity compensation strategies according to the magnitude of displacement deviation; when the deviation is small, fine-tuning compensation is used to maintain processing continuity, and when the deviation is large, a segmented compensation strategy of pause-repositioning-continue processing is adopted; establishing a compensation effect evaluation mechanism, judging the compensation effect by real-time monitoring of the displacement change trend after compensation; when multiple consecutive compensations still cannot control the deviation within the allowable range, a global replanning program is automatically triggered to recalculate the processing path of the entire remaining area to ensure the stability and accuracy of subsequent processing.

[0092] By introducing adaptive damping control, vibration characteristics and damping coefficients are analyzed using a dynamic response model of the fabric during processing. The processing speed and tool pressure are adjusted in real time to actively suppress vibration and oscillation. Multi-level compensation thresholds are set based on displacement deviations. For small deviations, fine-tuning compensation is performed to maintain continuity; for large deviations, segmented compensation—pausing, repositioning, and resuming processing—is adopted to ensure trajectory stability. A compensation effect evaluation mechanism assesses the effectiveness of the correction, triggering global path replanning when necessary to guarantee the overall accuracy and reliability of subsequent processing, significantly improving the fabric processing quality and process stability under complex conditions.

[0093] This embodiment also includes: after processing each spiral ring, comparing the deviation between the actual processed contour and the designed contour, calculating the cumulative error value, and when the error exceeds the allowable range, making a global adjustment to the starting point position of the next spiral ring and adjusting the spiral contraction step size to ensure that the final processing accuracy meets the process requirements.

[0094] Specifically, the precision control process of the spiral processing path includes: after each spiral loop is processed, the actual contour data of the currently processed area is acquired through an image acquisition device; the actual contour data is registered and compared with the preset design contour data, and the normal deviation value and tangential deviation value between the two are calculated.

[0095] The average and maximum deviations of the current spiral ring are calculated based on the deviation values ​​of multiple measurement points, and the current deviation value is accumulated with the deviation value of the previous spiral ring to obtain the cumulative error value; when the cumulative error value exceeds the preset allowable error threshold, the main distribution direction and magnitude of the deviation are analyzed.

[0096] The global adjustment vector for the starting point of the next spiral ring is determined based on the direction of the deviation distribution. The global adjustment vector includes position compensation amounts along the X and Y axes of the fabric coordinate system. At the same time, the interlayer spacing parameter of the spiral shrinkage is dynamically adjusted according to the magnitude of the cumulative error value. When the cumulative error value is large, the interlayer spacing is reduced to improve the processing accuracy, and when the cumulative error value is small, the interlayer spacing is appropriately increased to improve the processing efficiency.

[0097] The adjusted starting point position and interlayer spacing parameters are applied to the path generation of subsequent spiral rings. The above deviation detection and adjustment process is repeated after each spiral ring is completed until all processing areas are completed and the final cumulative error value meets the process accuracy requirements.

[0098] In this embodiment, after each spiral loop is completed, the actual contour of the processed area is acquired and compared with the designed contour. The normal and tangential deviations are calculated in real time, and the average deviation, maximum deviation, and cumulative error are obtained based on multi-point measurement results. When the cumulative error exceeds the allowable threshold, the direction and magnitude of the deviation are analyzed, a global adjustment vector is generated, the starting point of the next spiral loop is compensated, and the interlayer spacing of the spiral contraction is dynamically adjusted according to the magnitude of the error to balance accuracy and efficiency. The overall effect is to achieve closed-loop regulation of spiral path generation and error control, ensuring stable contour accuracy of multi-layer fabrics during cutting or processing, avoiding trajectory deviation and uneven cutting caused by error accumulation, significantly improving processing quality and path control reliability, and providing strong process assurance for high-precision fabric processing.

[0099] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An intelligent path planning method for adaptive fabric cutting, characterized in that, include: Obtain the physical parameters and image data of multi-layer fabrics, and establish a friction model between fabric layers; Based on the interlayer friction model of the fabric, the stress component data of the local stress field generated by the processing tool on the surrounding fabric are calculated, and the stress transfer efficiency of the stress release of the completed area to the untreated area is obtained, so as to predict the interlayer relative displacement data of the multilayer fabric during the processing. Based on the interlayer relative displacement data, the stress distribution of the fabric during the processing is analyzed, high stress concentration areas and low stress areas are identified, and the redistribution of processing points is obtained. Based on the redistributed processing points, a spiral processing path is generated that advances layer by layer from the edge of the fabric to the center. The stress difference between adjacent processing points in the spiral processing path is controlled within a preset threshold. The process of generating a spiral shrinkage path includes: determining the outer contour boundary of the fabric and using the outer contour as the starting layer of the spiral path; setting the interlayer spacing parameters and angle step parameters of the spiral path; starting from the outer contour boundary, moving counterclockwise according to the preset angle step parameters, shrinking inward by one interlayer spacing for each angle step; during the path generation process, detecting the distance between the current path point and the redistributed processing points in real time; when the distance between the path point and the nearest processing point is less than a preset threshold, adding the processing point as a path node to the spiral path; continuing the spiral shrinkage until reaching the geometric center of the fabric, forming a complete spiral shrinkage path; verifying the stress difference of the generated spiral path to ensure that the stress difference between adjacent path nodes is within the preset threshold range; During the processing, when the interlayer relative displacement data exceeds the preset threshold, the displacement vector is decomposed into components along the processing direction and perpendicular to the processing direction, and the subsequent processing path is compensated for translation and rotation according to the component magnitude. The translation and rotation compensation for subsequent processing paths based on component magnitudes includes: real-time monitoring of displacement vector data at various points on the fabric during processing; when the magnitude of the displacement vector exceeds a preset threshold, extracting the projection component of the displacement vector in the current processing direction and the projection component perpendicular to the processing direction; performing translation compensation along the processing direction on subsequent processing path nodes based on the magnitude and direction of the projection component perpendicular to the processing direction; calculating the rotation angle deviation of the fabric based on the magnitude and direction of the projection component perpendicular to the processing direction; performing rotation transformation compensation on subsequent processing path nodes based on the rotation angle deviation; and applying the translation and rotation compensation to the remaining unprocessed path nodes to obtain the compensated processing path.

2. The intelligent path planning method for adaptive fabric cutting according to claim 1, characterized in that: The establishment of the interlayer friction model of the fabric includes: acquiring material density data, fiber orientation data, and surface roughness data between each two adjacent layers in the multilayer fabric; calculating the elastic modulus parameter and Poisson's ratio parameter of each layer of fabric based on the material density data and fiber orientation data; and using image recognition technology to analyze the surface roughness data to obtain the interlayer contact area distribution data and contact pressure distribution data. Based on the elastic modulus parameter, Poisson's ratio parameter, contact area distribution data, and contact pressure distribution data, a fabric interlayer friction model describing the static and dynamic friction coefficients between each layer of fabric is established.

3. The intelligent path planning method for adaptive fabric cutting according to claim 1, characterized in that: The specific process for acquiring the stress component data includes: acquiring the geometric parameters, processing speed parameters, and processing depth parameters of the processing tool; determining the contact boundary range with the fabric based on the geometric parameters; calculating the normal pressure distribution and tangential shear force distribution applied to the fabric according to the processing speed parameters and processing depth parameters; calculating the stress attenuation coefficient of the fabric within different radii of the contact point; and acquiring the stress component data of each point on the fabric within a preset range around the tool by combining the normal pressure distribution, tangential shear force distribution, and stress attenuation coefficient.

4. The intelligent path planning method for adaptive fabric cutting according to claim 1, characterized in that: The specific process for obtaining the stress transfer efficiency includes: for the treated area, analyzing the stress relaxation effect after material removal, calculating the degree of influence of stress redistribution on the adjacent untreated area; using the stress release amount of the treated area as a new boundary condition, calculating the disturbance to the stress state of the untreated area, and obtaining the stress transfer efficiency.

5. The intelligent path planning method for adaptive fabric cutting according to claim 1, characterized in that: The process of acquiring the inter-layer relative displacement data includes: The multi-layer fabric is divided into regular grid cells, and the initial coordinate data of each grid cell is obtained. Based on the stress component data and stress transfer efficiency, the resultant force vector of each grid cell is calculated. Combining the static friction coefficient and dynamic friction coefficient, it is determined whether interlayer slippage occurs in each grid cell. For grid cells that have slipped, the displacement increment is calculated based on the force vector and the elastic parameters of the fabric. The displacement increment is accumulated to the coordinate data of the corresponding grid cell to obtain the updated coordinate data. By comparing the coordinate differences of corresponding grid cells in adjacent fabric layers, the interlayer relative displacement data is obtained. The interlayer relative displacement data includes the displacement direction vector and the displacement amplitude.

6. An intelligent path planning system for adaptive fabric cutting, characterized in that, The method for intelligent path planning of adaptive fabric cutting as described in claim 1 includes: The model building module is used to acquire the physical parameters and image data of multi-layer fabrics and to build a friction model between the fabric layers. The interlayer data acquisition module is used to calculate stress component data and stress transfer efficiency based on the interlayer friction model of the fabric, and to predict the interlayer relative displacement data of multilayer fabrics during the processing. The processing point update module is used to analyze the stress distribution of the fabric during the processing based on the interlayer relative displacement data, identify high stress concentration areas and low stress areas, and obtain the redistribution of processing points. The processing path acquisition module is used to generate a spiral shrinkage processing path that advances layer by layer from the edge of the fabric to the center based on the redistributed processing points. The processing path update module is used to perform translation and rotation compensation on the subsequent processing path according to the component magnitude when the interlayer relative displacement data is detected to exceed the preset threshold during the processing.

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