Three-dimensional weaving method and system for lining cloth and computer program product

Abstract

The invention discloses a three-dimensional weaving method and system for lining cloth and a computer program product, and relates to the technical field of textile digitization, and the method comprises the following steps: carrying out simulation weaving according to a three-dimensional weaving scheme of the lining cloth to obtain simulation weaving lining cloth; performing multi-scale comparison on the simulated weaving lining cloth to obtain a lining cloth characteristic comparison result; carrying out taking risk detection on the simulated weaving lining cloth to obtain a lining cloth taking risk detection result; performing finish machining adaptability detection on the simulated weaving lining cloth to obtain a lining cloth processing adaptability detection result; and performing parameter optimization on the three-dimensional weaving scheme of the lining cloth to obtain a lining cloth weaving optimization strategy. The technical problems that in the prior art, traditional lining cloth weaving depends on experience trial and error, the development period is long, application scenes are difficult to accurately match, and quality risk prevention and control are insufficient are solved, and the technical effects that lining cloth design accuracy and risk prevention and control preposition are achieved, and therefore the production quality is improved are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of textile digitization, in particular to a three-dimensional weaving method, system and computer program product for lining cloth. BACKGROUND

[0002] Under the background of digital transformation of the textile industry, traditional lining cloth weaving relies on experience and trial and error and physical sampling, which has the disadvantages of long design cycle, serious material waste, unstable quality, etc. With the continuous improvement of the functional and adaptive requirements of the garment industry for lining cloth, the existing technology cannot accurately verify the design matching degree of micro-fiber structure, meso-weave structure and macro-morphology through single-scale detection, nor can it effectively predict the collapse, wear and tear and other risks of lining cloth in actual use, as well as the process adaptability problems in cutting, ironing and other finishing processes.

[0003] The existing technology has the technical problems of relying on experience and trial and error in traditional lining cloth weaving, long development cycle, and difficulty in accurately matching application scenarios and insufficient quality risk prevention and control. SUMMARY

[0004] The present application provides a three-dimensional weaving method, system and computer program product for lining cloth, which is used to solve the technical problems of relying on experience and trial and error in traditional lining cloth weaving, long development cycle, and difficulty in accurately matching application scenarios and insufficient quality risk prevention and control in the prior art.

[0005] In view of the above problems, the present application provides a three-dimensional weaving method, system and computer program product for lining cloth.

[0006] A first aspect of the embodiments of the present application provides a three-dimensional weaving method for lining cloth, the method comprising: According to the three-dimensional weaving scheme of the lining cloth, a simulated weaving is performed to obtain a simulated woven lining cloth; a multi-scale comparison is performed on the simulated woven lining cloth according to a lining cloth design scheme to obtain a lining cloth feature comparison result; a wear risk detection is performed on the simulated woven lining cloth according to a lining cloth application correlation layer to obtain a lining cloth wear risk detection result; a finishing adaptability detection is performed on the simulated woven lining cloth according to a lining cloth finishing space to obtain a lining cloth finishing adaptability detection result; and a parameter optimization is performed on the three-dimensional weaving scheme of the lining cloth according to the lining cloth feature comparison result, the lining cloth wear risk detection result and the lining cloth finishing adaptability detection result to obtain a lining cloth weaving optimization strategy.

[0007] A second aspect of the embodiments of the present application provides a three-dimensional weaving system for lining cloth, the system comprising: The simulation woven base cloth acquisition module is configured to simulate weaving according to the base cloth three-dimensional weaving scheme to obtain a simulation woven base cloth; the feature comparison result acquisition module is configured to compare the simulation woven base cloth with the base cloth design scheme in multiple scales to obtain a base cloth feature comparison result; the detection result acquisition module is configured to detect the simulation woven base cloth for wearing risk according to the base cloth application correlation layer to obtain a base cloth wearing risk detection result; the adaptability detection result acquisition module is configured to detect the simulation woven base cloth for finishing adaptability according to the base cloth finishing space to obtain a base cloth finishing adaptability detection result; and the base cloth weaving optimization strategy acquisition module is configured to optimize the base cloth three-dimensional weaving scheme according to the base cloth feature comparison result, the base cloth wearing risk detection result and the base cloth finishing adaptability detection result to obtain a base cloth weaving optimization strategy.

[0008] In a third aspect, the present application provides a computer program product storing a computer program, which is configured to execute the three-dimensional weaving method of the base cloth.

[0009] The one or more technical solutions provided in the present application have at least the following technical effects or advantages: The simulation woven base cloth acquisition module is configured to simulate weaving according to the base cloth three-dimensional weaving scheme to obtain a simulation woven base cloth; the feature comparison result acquisition module is configured to compare the simulation woven base cloth with the base cloth design scheme in multiple scales to obtain a base cloth feature comparison result; the detection result acquisition module is configured to detect the simulation woven base cloth for wearing risk according to the base cloth application correlation layer to obtain a base cloth wearing risk detection result; the adaptability detection result acquisition module is configured to detect the simulation woven base cloth for finishing adaptability according to the base cloth finishing space to obtain a base cloth finishing adaptability detection result; and the base cloth weaving optimization strategy acquisition module is configured to optimize the base cloth three-dimensional weaving scheme according to the base cloth feature comparison result, the base cloth wearing risk detection result and the base cloth finishing adaptability detection result to obtain a base cloth weaving optimization strategy. The technical effect of realizing the base cloth design precision and the risk prevention and control preposition is achieved, thereby improving the production quality. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0011] Figure 1 A flowchart of the three-dimensional weaving method of the base cloth provided by the embodiments of the present application.

[0012] Figure 2 A structural diagram of the three-dimensional weaving system of the base cloth provided by the embodiments of the present application.

[0013] Explanation of reference signs: analog woven base cloth acquisition module 10, feature comparison result acquisition module 20, detection result acquisition module 30, adaptive detection result acquisition module 40, base cloth weaving optimization strategy acquisition module 50. DETAILED DESCRIPTION

[0014] The present application provides a three-dimensional weaving method, system and computer program product for base cloth, which is used to solve the technical problems of long development cycle, difficulty in precise matching of application scenarios, and insufficient quality risk prevention and control in the prior art.

[0015] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0016] Embodiment one, as shown in the present application provides a three-dimensional weaving method for base cloth, which comprises: Figure 1 Step S100: analog weaving according to the three-dimensional weaving scheme of the base cloth to obtain an analog woven base cloth.

[0017] Specifically, a three-dimensional structure model of the base cloth is constructed by computer-aided design (CAD) software, initial weaving parameters such as yarn material, density, and interweaving rules are set, the mechanical process of yarn interweaving is simulated based on finite element analysis (FEA) or discrete element method (DEM), the friction force, tension distribution and deformation trend between yarns are iteratively calculated by algorithm, a virtual analog woven base cloth is dynamically generated, the digital mapping of the actual weaving process is realized, and a verifiable virtual sample is provided for subsequent multi-dimensional detection and process optimization.

[0018] Step S200: multi-scale comparison of the analog woven base cloth according to the base cloth design scheme to obtain a base cloth feature comparison result.

[0019] ​Specifically, according to the lining design scheme, the generated simulated woven lining is disassembled and compared in multiple dimensions. Specifically, the simulated woven lining is divided into a micro layer (nanoscale structure such as fiber diameter, molecular chain arrangement), a meso layer (millimeter-level structure such as warp and weft density, fabric organization pattern), and a macro layer (centimeter-level form such as width, grammage, surface flatness), and respectively mapped and analyzed with the corresponding indicators in the design scheme. For example, in the micro layer, the fiber diameter deviation is compared through scanning electron microscope simulation data, in the meso layer, the difference between the design value and the interlacing angle of the warp and weft yarns is detected by image recognition technology, and in the macro layer, the width tolerance and grammage uniformity are verified by size measurement algorithm. Through cross-scale feature matching and difference analysis, the lining feature comparison results covering microstructure consistency, meso texture matching degree, and macro form compliance degree are generated, providing fine problem positioning basis for subsequent weaving scheme parameter adjustment, and ensuring that the simulated woven lining meets the design expectations in each level structure.

[0020] Step S300: detecting the wearing risk of the simulated woven lining according to the lining application associated layer, to obtain a lining wearing risk detection result.

[0021] Specifically, a composite wearing structure model is constructed based on the lining application associated layer (including the associated fabric layer and the lining layer), to simulate the stress state and dynamic behavior of the lining in actual wearing scenarios. First, by analyzing the wearing application scenarios (such as daily wear, sports stretching, etc.), various wearing stress conditions (such as active compression, passive extrusion, friction slip, etc.) are extracted, and the dynamic simulation of the wearing structure model is performed to obtain structure deformation simulation data and response data. Next, for the collapse risk, the yield feature set (such as compression deformation threshold, rebound performance) of the lining under active / passive compression is identified, and multi-point risk evaluation is performed to generate a collapse risk prediction result; for the wear and tear risk, the structure response data generated by dynamic simulation is input into a preset wear and tear risk analysis model to calculate the corresponding risk coefficient. Finally, the collapse risk prediction result and the wear and tear risk coefficient are integrated into the lining wearing risk detection result, to quantitatively evaluate the structural stability and durability of the lining in the actual wearing process, and to provide safety and reliability basis for weaving scheme optimization.

[0022] Step S400: detecting the processing adaptability of the simulated woven lining according to the lining finishing space, to obtain a lining processing adaptability detection result.

[0023] Specifically, for the three core scenes of lining finishing (cutting processing, ironing and shaping, hot melt bonding), multiple sets of simulation detection schemes are designed. First, in the cutting processing adaptability detection, according to the K cutting processing schemes (K≥2) matched with the lining design scheme, the simulated woven lining is virtually cut, and K cutting processing quality coefficients are generated by evaluating the cutting edge flatness, size accuracy and other indicators. The reciprocal of the variance of the quality coefficient is taken as the first coefficient of the lining processing adaptability, reflecting the stability of the cutting scheme; secondly, in the ironing and shaping adaptability detection, the deformation of the lining under different temperature, pressure and time parameters is simulated, and the designed shaping size and surface flatness are compared to generate the second coefficient of the lining processing adaptability; finally, in the hot melt bonding adaptability detection, the process conditions such as hot melt temperature and pressing time are simulated, and the bonding strength and wrinkle resistance of the lining and the fabric are analyzed to generate the third coefficient of the lining processing adaptability. The three types of coefficients are integrated into the lining processing adaptability detection results to comprehensively evaluate the adaptability of the simulated woven lining in the subsequent finishing process, identify potential problems affecting processing efficiency and product quality, and provide processing feasibility basis for weaving process parameter optimization.

[0024] Step S500: According to the lining feature comparison results, the lining wear risk detection results and the lining processing adaptability detection results, the parameters of the lining three-dimensional weaving scheme are optimized to obtain a lining weaving optimization strategy.

[0025] Specifically, the output lining feature comparison results (covering microstructure, mesostructure and macrostructure difference data), the generated wear risk detection results (including collapse risk level and wear and tear risk coefficient) and the obtained processing adaptability detection results (cutting, ironing and hot melt bonding adaptability coefficients) are integrated to construct a multi-objective optimization model. With the feature matching degree compliance rate, risk coefficient threshold and processing adaptability coefficient as constraint conditions, the key parameters (such as yarn material ratio, warp and weft density, loom tension and speed) in the lining three-dimensional weaving scheme are iteratively optimized by particle swarm optimization algorithm or genetic algorithm. For example, if the microfiber diameter deviation exceeds the design range and the cutting processing adaptability coefficient is low, the yarn twist and weaving tension parameters are adjusted first; if the wear and tear risk coefficient is too high, the fabric structure is optimized to enhance the wear resistance. Finally, the lining weaving optimization strategy considering product structure precision, wear safety and processing adaptability is output, realizing accurate adjustment of process parameters from simulation weaving to actual production, and improving the comprehensive performance and production efficiency of the lining product.

[0026] In one possible implementation manner, step S200 further includes: Step S210: The simulated woven lining is unfolded in multiple scales to obtain a simulated lining micro layer, a simulated lining meso layer and a simulated lining macro layer.

[0027] Step S220: Micro-mapping comparison of the simulated lining cloth micro-layer according to the lining cloth design scheme, to obtain a lining cloth micro-comparison result.

[0028] Step S230: Meso-mapping comparison of the simulated lining cloth meso-layer according to the lining cloth design scheme, to obtain a lining cloth meso-comparison result.

[0029] Step S240: Macro-mapping comparison of the simulated lining cloth macro-layer according to the lining cloth design scheme, to obtain a lining cloth macro-comparison result, and combining the lining cloth micro-comparison result and the lining cloth meso-comparison result, to generate the lining cloth feature comparison result.

[0030] Specifically, the simulated woven lining cloth is hierarchically deconstructed through multi-scale modeling technology: firstly, the lining cloth structure is decomposed into a micro-layer (yarn and fiber level structure, focusing on features such as filament diameter, twist, fiber orientation, etc.), a meso-layer (fabric organization unit level structure, analyzing interlacing rules of warp and weft yarns, organization cycle parameters, etc.), and a macro-layer (overall physical form, covering size accuracy, surface flatness, contour shape, etc.) by using material micro-mechanics theory. Through three-dimensional grid division and feature extraction algorithm, an independent digital twin model is generated for each level, wherein the micro-layer resolution reaches micrometer level, the meso-layer resolution reaches sub-millimeter level, and the macro-layer covers the overall geometric size of the lining cloth, forming a cross-scale description system from fiber to fabric, and providing a structured data foundation for subsequent multi-dimensional feature comparison.

[0031] With high-precision microscopic equipment (such as electron microscope), the image of the micro-layer of the simulation lining cloth is taken, and the actual morphology data of the fine structure (such as fiber diameter, arrangement direction, yarn interweaving mode, pore distribution, etc.) is obtained. Then, the actual data is compared with the preset microstructure standard in the lining cloth design scheme one by one: fiber feature comparison, check whether the actual fiber diameter is within the design range, whether the fiber is broken, uneven in thickness, etc.; observe whether the fiber arrangement direction conforms to the expected orientation of the design (such as whether it is uniformly and orderly distributed or inclined at a specific angle). Yarn interweaving comparison, analyze whether the shape of the yarn interweaving point is consistent with the design (such as the tightness of interweaving, the shape of the node), whether there are abnormal loose or excessive winding conditions. Pore structure comparison, count whether the size, density and distribution of the pores meet the design porosity standard, whether there are defects such as local pores being too large or too small, uneven distribution, etc. During the comparison process, record the deviation of the actual data from the design standard (such as fiber diameter exceeding the standard ±5%, fiber orientation deviating from the design angle ±10°, etc.), and identify the microstructure defects that do not meet the design requirements (such as abnormal fiber twisting, pore concentration distribution area). Finally, all the comparison results are integrated to form a micro comparison report of the lining cloth, to clearly show the conformity of the micro-layer of the simulation lining cloth with the design scheme, mark the specific difference points and defect positions, and provide a basis for subsequent optimization of the weaving scheme parameters.

[0032] The process of obtaining the interlining comparison results according to the interlining design scheme is as follows: first, the structure data of the interlining layer of the simulated woven interlining is obtained through optical microscopy or industrial CT and other detection equipment, including yarn linear density, warp and weft density, fabric organization (such as plain weave, twill, satin), mass per unit area and other mesoscale characteristic parameters. Then, compare these actual parameters with the mesoscale structure standards in the interlining design scheme: yarn structure comparison, check whether the yarn linear density meets the design specifications (such as whether the error is controlled within ±3%), whether the warp and weft density is consistent with the design value (such as the deviation range of the number of warp and weft per inch), whether there are problems such as lack of warp, lack of weft or uneven yarn thickness. Fabric organization comparison, analyze whether the actual fabric organization is consistent with the design requirements (such as whether the interlacing rule of plain weave is accurate), whether the distribution of warp and weft interlacing points is uniform, whether there are organization disorder or purling and other defects. Physical performance comparison, test the tensile strength, tear strength, air permeability and other mesoscale physical performance indicators of the simulated interlining, and compare them with the target values in the design scheme (such as tensile strength ≥200N, air permeability ≤50mm / s) to evaluate the performance compliance. During the comparison process, record the parameter deviation (such as warp and weft density deviation ±5 roots / inch) and defect type (such as broken weft, loose warp), and quantitatively analyze the impact on the functionality of the interlining (such as insufficient strength may cause garment deformation). Finally, based on the comparison results of the mesoscale structure parameters and physical performance, generate an interlining mesoscale comparison report to determine the conformity of the simulated interlining mesoscale layer to the design scheme, mark specific differences (such as low warp and weft density in a certain area) and performance defects, and provide a basis for adjusting the weaving process parameters.

[0033] The overall evaluation of the consistency of the interlining design is achieved through multi-scale data fusion and cross-level correlation analysis: first, the macro layer of the simulated interlining is scanned by three-dimensional visual measurement technology, and the macro features such as contour shape, size accuracy and surface texture are extracted, and the geometric matching algorithm is used to compare with the theoretical model of the design scheme point by point, and the macro comparison results including shape deviation distribution and key size error are generated. At the same time, call the yarn structure parameter matching data of the micro layer (such as fiber diameter deviation, twist consistency) and the fabric organization unit analysis results of the mesoscale layer (such as interlacing point position error, organization cycle integrity), and construct a cross-scale feature data set from nanometer to millimeter. Through principal component analysis (PCA) to reduce dimension redundancy, use evidence theory to fuse multi-source heterogeneous data, assign weights to micro, meso and macro levels respectively and perform weighted calculation, and finally generate an interlining feature comparison report integrating the three-level comparison results, covering microstructure difference details, mesoscale organization defect positioning, macro shape deviation visualization and comprehensive matching degree score, forming a systematic evaluation of the consistency of the simulated woven interlining and the design scheme, and providing multi-level data support for the subsequent parameter optimization of the three-dimensional weaving scheme.

[0034] In one possible implementation, step S300 further includes: Step S310: constructing a garment structure model according to the application of the lining application associated layer and the simulated woven lining, the lining application associated layer including a lining associated fabric layer and a lining associated lining layer.

[0035] Step S320: predicting the collapse risk of the simulated woven lining according to the garment structure model to obtain a lining collapse risk prediction result.

[0036] Step S330: predicting the wear and tear peeling risk of the simulated woven lining according to the garment structure model to obtain a wear and tear peeling risk prediction result.

[0037] Step S340: adding the lining collapse risk prediction result and the wear and tear peeling risk prediction result to the lining garment risk detection result.

[0038] Specifically, the simulated woven lining is digitally fitted with the virtually constructed lining associated fabric layer and lining associated lining layer through a three-dimensional modeling software (such as AutoCAD, SolidWorks) to form a garment structure model containing three layers. In specific implementation, the physical property parameters of each layer of material are first input: the fabric layer is set with parameters such as grammage, thickness, and friction coefficient to simulate the external fabric texture, the lining layer is input with data such as elastic modulus and air permeability to match the close-fitting wearing characteristics, and the simulated woven lining is imported with generated parameters such as fiber density and fabric porosity. Then the interlayer action relationship is defined, the composite mechanical behavior of the lining and the fabric / lining is simulated by setting parameters such as adhesion strength threshold and interfacial friction coefficient, for example, the spring-damper model is used to simulate the interlayer slip resistance, and the collaborative deformation simulation of each layer structure is realized through finite element meshing technology, and finally a multi-level mechanical analysis model is constructed which can truly reflect the actual garment environment of the lining.

[0039] Based on the lining application associated layer, a garment structure model containing fabric and lining is constructed, typical stress characteristics such as joint movement and sitting posture compression in the garment wearing scene are extracted through computer vision technology, and multiple dynamic stress conditions (such as periodic pressure and continuous static load) are generated; then the finite element method (FEM) is used to apply anisotropic load to the garment structure model, the three-dimensional deformation process of the lining under different stress conditions is simulated, and the structure deformation simulation data is obtained; further, the active compression yield characteristics (such as the fiber buckling starting point) and the passive compression yield characteristics (such as the overall fabric collapse threshold) in the data are identified through the convolutional neural network (CNN), and a feature vector matrix is constructed; finally, based on the support vector machine (SVM) algorithm, in combination with the preset collapse risk threshold, the multi-point collapse probability of the simulated woven lining is quantitatively evaluated, and a collapse risk prediction result containing risk level distribution is generated.

[0040] Based on the constructed garment structure model (including lining associated fabric layer, simulated woven lining, lining associated interlining layer), simulate various actual garment motions (such as friction generated by arm swing, stretching caused by clothing folding, external force tearing, etc.), generate structure simulation response data through physical simulation engine, including inter-fiber friction, inter-layer shear force, stress-strain distribution and other dynamic mechanical parameters. Input these data into the pre-trained lining wear and tear risk analysis model to calculate the wear and tear risk coefficient of each region, and quantitatively evaluate the fiber wear degree, pilling probability and other indicators; at the same time, input the response data into the lining peeling risk analysis model to analyze the adhesion interface performance between the lining and the associated fabric layer and interlining layer, and obtain the peeling risk coefficient (such as inter-layer adhesion strength attenuation rate, interface slip probability). Finally, integrate the wear and tear risk coefficient and the peeling risk coefficient of each region to generate wear and tear peeling risk prediction results including risk type, risk value and distribution location, and intuitively present the weak links of lining durability in long-term use (such as easy-to-wear shoulder, easy-to-peel seam position), provide data support for optimizing weaving process (such as adjusting yarn wear resistance, enhancing inter-layer adhesion process), and ensure that the lining has reliable structural stability and service life in actual garment use scenarios.

[0041] Integrate the generated lining collapse risk prediction results (including high-risk area location, collapse level and mechanical weak point) and the obtained wear and tear peeling risk prediction results (including wear and tear risk coefficient, peeling risk coefficient and distribution location) to form a complete lining garment use risk detection result. The result is presented in the form of data report or visual map, for example, by labeling the collapse risk level of each region of the lining with a heat map (such as a red area indicating a high-risk collapse area), and superimposing the wear and tear peeling risk value (such as a numerical label indicating the wear and tear coefficient). During the integration process, automatically associate the risk type with the specific structure characteristics, for example, associate the high collapse risk of the shoulder with the low density of the warp and weft in this area, or associate the high peeling risk of the sleeve with the insufficient inter-layer adhesion process, provide multi-dimensional risk index for subsequent weaving scheme optimization, and ensure that the lining meets the safety, durability and comfort requirements in actual garment use scenarios.

[0042] In one possible implementation manner, step S320 further includes: Step S321: garment stress feature mining is performed on the garment application scenario of the garment structure model, and a plurality of garment stress conditions are constructed.

[0043] Step S322: according to the plurality of garment stress conditions, respectively performing dynamic simulation on the garment structure model, and obtaining a plurality of structure deformation simulation data.

[0044] Step S323: according to the plurality of structure deformation simulation data, lining active compression yield feature recognition is performed, and an active compression yield feature set is obtained.

[0045] Step S324: Passive compression yield feature identification is performed according to the plurality of structure deformation simulation data, and a passive compression yield feature set is obtained.

[0046] Step S325: Multi-point collapse risk evaluation is performed on the simulation woven lining according to the active compression yield feature set and the passive compression yield feature set, and a lining collapse risk prediction result is generated.

[0047] Specifically, for the actual application scenarios of the wearing structure model (such as daily wearing of clothing lining, sports stretching or extrusion load bearing scenarios of bag lining), the wearing stress characteristics in different scenarios are mined through sensor data acquisition, ergonomics analysis and industry standard research. For example, in the daily wearing scenario, the vertical static pressure (force size 10-20 kPa, direction vertically downward) borne by the waist lining and the dynamic shear force (force size 5-15 N, direction periodically changing) generated by the arm swing of the shoulder lining are extracted; in the sports scenario, the bending stress (force size 20-30 kPa, direction changing along the joint activity track) borne by the elbow lining is analyzed; in the bag scenario, the impact load (force size peak value 50-100 N, random direction) and the continuous extrusion stress (force size 20-50 kPa, multi-directional uniformity) borne by the lining in the transportation process are collected. By integrating the parameters such as force size, direction and action time, a plurality of differential wearing stress conditions are constructed, such as static vertical pressure (15 kPa, vertically downward), dynamic shear force (10 N, ±45° cycle), impact load (80 N, random direction), etc., to provide diversified mechanical boundary conditions for subsequent dynamic simulation.

[0048] Based on the plurality of wearing stress conditions (such as static vertical pressure, dynamic shear force, impact load, etc.) constructed, a dynamic simulation is performed on the wearing structure model by using finite element analysis (FEA) software (such as ANSYS, ABAQUS) or multi-body dynamics simulation tools (such as ADAMS). In specific implementation, each wearing stress condition is first converted into a boundary condition of the simulation model, for example, for the static vertical pressure condition, a uniform load (such as 15 kPa vertically downward) is applied on the surface of the model; for the dynamic shear force condition, a periodic changing tangential load (such as 10 N, ±45° cycle) is set. The model is discretized into finite elements by using meshing technology, and an implicit or explicit solver is enabled to calculate the response of the structure under the load, and the displacement, strain and stress data of each node are collected in real time to generate structure deformation simulation data, including the thickness compression amount of each region of the lining (such as 0.2 mm compression in the waist region), three-dimensional displacement field (such as ±1.5 mm displacement in the shoulder region), stress cloud diagram (such as 25 MPa in the stress concentration region of the sleeve) and the like. The simulation program is independently run for each stress condition to ensure that the data covers the mechanical response in different scenarios and provides multi-dimensional deformation data support for subsequent yield feature identification.

[0049] From the acquired multiple structure deformation simulation data, dynamic data corresponding to human active motion scenes (such as bending, lifting arms, walking, etc.) are screened out, and time domain and frequency domain features are extracted through digital signal processing algorithms (such as fast Fourier transform, wavelet analysis). In specific implementation, first, the determination threshold of active compression (such as pressure change frequency > 0.1 Hz and duration < 5 s) is set, and the deformation data under the active compression scene is separated, such as the dynamic pressure waveform of the waist lining cloth under the bending action. Then, the feature engineering method in machine learning is used to extract features from the waveform data: calculate the compression deformation threshold (such as the pressure value when the maximum compression amount reaches 15% of the original thickness), the rebound speed (50% deformation recovery, time < 0.3 s), the energy absorption coefficient (mechanical energy absorbed per unit volume > 20J / m 3 ) and other parameters. Through clustering algorithm (such as K-means), the feature parameters are grouped to generate active compression yield feature set, for example, containing high rebound type (rebound speed > 10mm / s), strong support type (compression threshold > 20kPa) and other feature labels, providing active kinetic performance indicators for subsequent collapse risk evaluation.

[0050] From the generated structure deformation simulation data, static or quasi-static data of passive compression scenes (such as external extrusion, static bearing, etc.) are screened out, and feature parameters are extracted through mechanical property analysis algorithm. In specific implementation, first, the data under passive compression conditions are identified through load type markers (such as duration > 10 s and frequency < 0.01 Hz), such as the deformation data of the luggage lining cloth under the continuous heavy extrusion. Then, statistical analysis is performed on the data: calculate the permanent deformation rate (the ratio of residual deformation to maximum deformation after unloading, such as > 5% is considered high risk), the creep resistance limit (the critical pressure value under which the deformation growth rate is < 0.1mm / h under continuous load), the stress relaxation coefficient (stress attenuation amplitude < 5kPa / h per unit time) and other indicators. Threshold determination method is used to classify the feature parameters to generate passive compression yield feature set, for example, labeled high stability (permanent deformation rate < 3%), creep resistance type (creep resistance limit > 30kPa) and other feature labels, providing quantitative basis for evaluating the structure durability of the lining cloth under passive load.

[0051] The generated active compression yield feature set (such as rebound speed, compression threshold) and the obtained passive compression yield feature set (such as permanent deformation rate, creep resistance limit) are spatially mapped with the preset key points (such as collar, shoulder, waist, sleeve opening, etc. high frequency stress area) of the simulated woven lining. In specific implementation, through the finite element grid node number association point coordinate, the fuzzy comprehensive evaluation algorithm is used to construct the risk assessment matrix: the collapse risk value of each point is calculated by the active feature weight (such as 40%) and the passive feature weight (such as 60%), for example, the active compression rebound speed (12mm / s) of the waist point corresponds to the low risk level, the passive permanent deformation rate (4%) corresponds to the medium risk level, and the comprehensive risk value is determined as moderate risk. At the same time, the heat map visualization technology (such as GIS spatial analysis) is used to mark the risk level of each point, and the collapse risk prediction result report containing three-dimensional coordinates, risk level (mild / moderate / severe) and feature parameter deviation value is generated, for example, the shoulder point (X=100mm, Y=80mm) has low active compression threshold (measured 18kPa < designed value 20kPa) and insufficient passive creep resistance limit (28kPa < 30kPa), and the comprehensive evaluation is severe collapse risk, which provides accurate point-level improvement guidance for subsequent weaving process optimization.

[0052] In one possible implementation manner, step S330 further includes: Step S331: dynamically simulating the wearing structure model according to the plurality of wearing actions to obtain a plurality of structure simulation response data.

[0053] Step S332: inputting the plurality of structure simulation response data into a lining wear risk analysis model to obtain a plurality of lining wear risk coefficients.

[0054] Step S333: inputting the plurality of structure simulation response data into a lining peeling risk analysis model to obtain a plurality of lining peeling risk coefficients.

[0055] Step S334: adding the plurality of lining wear risk coefficients and the plurality of lining peeling risk coefficients to the wear and peeling risk prediction result.

[0056] Specifically, joint motion data of typical human wearing actions (e.g. arm swing when walking, bending to pick up objects, and changing sitting posture) are collected by motion capture devices (e.g. Vicon optical motion capture system), and combined with multi-body dynamics software (e.g. AnyBody) to build a motion-driven wearing structure model dynamic simulation scene. In specific implementation, the action is decomposed into a series of key frames, and boundary conditions are set for each key frame: for example, in the arm swing action, a periodic shear load with a frequency of 1-2 Hz and an amplitude of ±45° is applied to the shoulder cloth area, and a bending stress with a curvature radius of 10-15 mm is introduced at the elbow cloth area. Using finite element analysis tools (e.g. ABAQUS) for explicit dynamic solving, real-time capture of model stress and strain distribution (e.g. stress cloud peak position) within the action cycle, fiber contact friction data (e.g. friction work accumulation), interlayer relative displacement (e.g. interface slip amount of 0.1 mm duration), and other structural simulation response data form a dynamic data set containing time series. Each wearing action runs the simulation program independently to ensure that the data covers the mechanical response characteristics of high-frequency use scenarios, providing accurate raw data support for the quantitative analysis of wear and tear and peeling risk.

[0057] A random forest algorithm is used to build a cloth wear risk analysis model: the output structural simulation response data (e.g. stress peak, contact frequency, slip distance, friction work, etc. 20-dimensional features) are input into an ensemble model composed of 500 decision trees. Each decision tree uses Bootstrap to extract 70% of the samples from the original data during training, and randomly selects 15 features for node splitting (split criterion is Gini impurity). The model generates a decision tree with a maximum depth of 12 through recursive partitioning, and the minimum number of samples in the leaf node is set to 5. In the reasoning stage, each tree independently predicts the wear risk level (0-4 levels) based on the input features, and finally converts the classification results into a continuous risk coefficient in the range of 0-1 (e.g. a risk level of 3 corresponds to a coefficient of 0.75) through a weighted voting mechanism.

[0058] A graph neural network (GNN) combined with an attention mechanism is used to build a cloth peeling risk analysis model: the structural simulation response data is converted into a graph structure with cloth grid nodes as vertices and node connections as edges, each node contains stress tensor, temperature, etc. features, and edge features are relative displacement and interface properties. The model extracts spatial features through a 3-layer graph convolution network (GCN) to capture the interaction between layers; at the same time, it applies a spatio-temporal attention mechanism to dynamically weight key features (e.g. high stress areas, weak interface points). Then, use the gated recurrent unit (GRU) to process time series data and learn the cumulative effect of load history on peeling risk. Finally, a multi-layer perceptron (MLP) is used to output a peeling risk coefficient in the range of 0-1, for example, when the stress concentration coefficient of the node at the seam exceeds the threshold value and the interface slip amount continues to increase, the model outputs a higher risk coefficient, corresponding to a high peeling risk level.

[0059] The output lining wear risk coefficient and peel risk coefficient are integrated in multiple dimensions: First, a three-dimensional grid coordinate system for the lining is established, and the two types of risk coefficients are mapped to corresponding nodes (e.g., shoulder point coordinates X=100mm, Y=80mm); then, a composite risk matrix is ​​generated through tensor product operation, with each element containing wear risk value, peel risk value, and spatial coordinates; next, the analytic hierarchy process (AHP) is used to calculate the comprehensive risk index, and the weight matrix is ​​dynamically adjusted according to the functional importance of different areas of the lining (e.g., wear weight 0.6 and peel weight 0.4 for the collar area); finally, a multi-dimensional prediction result report is generated, including spatial heat map, risk level (mild / moderate / severe), and key influencing factors (e.g., stress concentration factor, interface temperature). For example, the waist area (X=85mm, Y=120mm) has a wear risk coefficient of 0.75 due to high-frequency friction and a peel risk coefficient of 0.68 due to interlayer shear stress, and is comprehensively rated as moderate risk, providing accurate data support for lining structure optimization.

[0060] In one possible implementation, step S400 further includes: Step S410: The interlining finishing space includes an interlining cutting space, an interlining ironing and shaping space, and an interlining heat-melting bonding space.

[0061] Step S420: Perform a cutting and processing adaptability test on the simulated woven lining based on the lining cutting and processing space to obtain the first coefficient of lining processing adaptability.

[0062] Step S430: Perform ironing and shaping adaptability test on the simulated woven interlining according to the ironing and shaping space of the interlining to obtain the second coefficient of interlining processing adaptability.

[0063] Step S440: Perform a heat-melt bonding adaptability test on the simulated woven lining based on the heat-melt bonding space of the lining to obtain the third coefficient of lining processing adaptability.

[0064] Step S450: Add the first coefficient, the second coefficient, and the third coefficient of lining processing adaptability to the lining processing adaptability test result.

[0065] Specifically, the lining finishing space refers to the process environment collection for subsequent fine processing of the lining after the completion of simulated weaving. It is specifically divided into three functional modules: lining cutting processing space, covering laser cutting, numerical control blade cutting and other process scenarios, involving cutting path planning, tool parameters (such as laser power, blade sharpness), material cutting resistance and other elements, for simulated evaluation of the edge quality (such as burr rate, heat damage degree) of the lining under different cutting processes. Lining ironing and shaping space focuses on process simulation in high temperature and high pressure environment, including ironing temperature (80-220℃), pressure (5-15kPa), residence time (3-10 seconds) and other parameters, for analyzing the heat plastic deformation ability, dimensional stability (such as shrinkage) and surface flatness of the lining in the ironing process. Lining hot melt bonding space is aimed at the composite process of lining and fabric, involving hot melt adhesive type (such as EVA, PA), glue coating amount (20-50g / m 2 ), bonding temperature (120-180℃), pressure (10-30kPa) and other parameters, for evaluating the adhesive flowability, interfacial bonding strength (such as peel strength) and wash resistance. The three spaces are modeled by digitalizing process parameters, building a finishing simulation environment covering the whole process of cutting, shaping and bonding, and providing a standardized evaluation framework for subsequent adaptability testing.

[0066] Adaptability testing of lining cutting processing is realized through multi-scheme comparison and statistical analysis: first, based on the matching analysis of lining design scheme (such as structure, edge precision requirement) and cutting processing space parameters (such as tool type, cutting speed), K feasible cutting schemes (such as laser cutting scheme, mechanical blade cutting scheme, etc.) are generated by using constraint satisfaction algorithm. Then, for each matching scheme, a finite element model is constructed to simulate the interaction process of the tool and the fabric, and K simulation cutting results containing stress distribution and fiber fracture trajectory are output. Next, an evaluation index system is established from three dimensions of edge quality (such as hair index, cut flatness), dimensional accuracy (such as cutting deviation rate) and material loss rate, and fuzzy comprehensive evaluation method is used to calculate the cutting processing quality coefficient of each scheme. Finally, the reciprocal of the variance of the K coefficients is taken as the first coefficient of adaptability. The smaller the variance, the more stable the performance of each scheme, the higher the coefficient value (tending to 1), indicating that the lining has stronger processing adaptability in the current cutting space; on the contrary, the larger the variance, the lower the coefficient, suggesting that the cutting parameters need to be optimized or the lining design needs to be adjusted.

[0067] Based on the actual process conditions of the lining cloth ironing and setting space, a thermal-mechanical coupling simulation system was constructed to simulate the adaptability of woven lining cloth. By establishing a three-dimensional heat conduction equation, combined with the thermal conductivity and specific heat capacity of the lining cloth material, the dynamic changes of the temperature field when the iron contacts the lining cloth were simulated. At the same time, the viscoelastic mechanics model was used to analyze the deformation and stress relaxation process of the lining cloth fibers under pressure. The temperature field distribution data and mechanical response data were input into the double-channel neural network: the temperature channel uses convolutional neural network to extract thermal diffusion features and capture the distribution of high-temperature areas and heat transfer rules; the mechanical channel uses recurrent neural network to analyze the evolution of stress and strain over time and excavate the deformation trend of the fibers. Finally, through the full connection layer, the two types of features are fused, and the second coefficient of lining cloth processing adaptability in the 0-1 interval is output through the activation function, which quantifies the adaptability of the lining cloth in the ironing and setting process, providing data support for subsequent production process optimization.

[0068] A neural network model based on physical constraints was used to detect the adaptability of lining cloth hot melt bonding: First, the Navier-Stokes equation model of hot melt adhesive film flow was constructed to simulate the viscosity changes and spreading process of the adhesive film under different temperatures (120-180℃) and pressures (10-30kPa), and extract spatial features such as adhesive film thickness uniformity and penetration depth. At the same time, the Johnson-Kendall-Roberts (JKR) model of interfacial bonding was established to calculate the adhesion energy and peel strength of the bonding interface. The fluid mechanics simulation data (such as velocity field, pressure field matrix) and interface mechanics data (such as stress-displacement curve) were input into the double-channel convolutional long short-term memory network (ConvLSTM): the spatial feature channel captures the details of the adhesive film distribution through 3 layers of dilated convolution, and the time series feature channel learns the adhesive film curing process under the coupling action of temperature and pressure through LSTM units; the model embeds the thermodynamic constitutive equation of hot melt adhesive as a loss function constraint term to ensure that the prediction results meet the physical laws such as the positive correlation between bonding strength and adhesive crosslinking degree. Finally, through the full connection layer, the adaptability coefficient in the 0-1 interval is output, for example, when the simulation shows that the adhesive film penetration uniformity is >90%, the peel strength reaches the industry standard threshold, and the number of wash cycles meets the requirements, the model outputs a higher coefficient, quantifying the adaptability of the lining cloth and the hot melt process.

[0069] The first, second and third coefficients of the lining cloth processing adaptability are fused and visually presented in multiple dimensions: first, a three-dimensional tensor space is constructed, the cutting adaptability coefficient is mapped to the X axis (cutting dimension), the ironing adaptability coefficient is mapped to the Y axis (shaping dimension), and the hot melt bonding adaptability coefficient is mapped to the Z axis (composite dimension), forming a Cartesian coordinate system based on the process dimension; then, the coupling relationship between the coefficients (such as the influence weight of cutting quality on hot melt effect) is extracted through tensor decomposition technology, generating a composite adaptability matrix containing 12 feature channels; then, the coefficient matrix is divided into 5 risk levels (such as level 1: high adaptability in the whole process; level 5: high risk in multiple links) by applying a hierarchical clustering algorithm, and a risk thermodynamic map of the lining cloth surface is generated by Delaunay triangulation; finally, a detection result report containing a three-dimensional risk cloud map, process link importance ranking, and weak point improvement suggestions is output.

[0070] In one possible implementation manner, step S420 further includes: Step S421: performing matching analysis on the lining cloth cutting processing space according to the lining cloth design scheme, to obtain K matching cutting processing schemes, K being a positive integer greater than 1.

[0071] Step S422: performing simulated cutting processing on the simulated woven lining cloth according to the K matching cutting processing schemes, to obtain K simulated cutting lining cloths.

[0072] Step S423: performing cutting processing quality evaluation on the K simulated cutting lining cloths, to obtain K cutting processing quality coefficients.

[0073] Step S424: calculating the reciprocal of the variance of the K cutting processing quality coefficients, to generate the first coefficient of the lining cloth processing adaptability.

[0074] Specifically, a multi-objective constraint optimization algorithm is used to realize intelligent matching of the lining cloth design scheme and the cutting processing space: first, the lining cloth design parameters (such as geometric profile, material properties) are converted into a set of constraint conditions (such as minimum curvature radius ≥ 5 mm, yarn breaking strength ≥ 20 cN / tex), and a capability matrix of the cutting processing space (containing 15 dimensions such as tool type, cutting speed range, precision limit) is constructed. Through a constraint satisfaction problem (CSP) solver, the feasible solution space that satisfies all design constraints is searched in the process parameter domain, and a non-dominated sorting genetic algorithm (NSGA-II) is used for multi-objective optimization, taking processing efficiency, cost and quality as optimization objectives, to generate K Pareto optimal solutions as matching cutting schemes.

[0075] A digital model of the simulated woven backing fabric is constructed by three-dimensional textile simulation software (such as TexGen, WeaveCAD), and based on the material parameters (such as grammage, yarn density, breaking strength) and structural parameters (such as the number of weave cycles, shrinkage) in the backing fabric design scheme, K matching cutting path algorithms (such as straight line cutting, curve fitting cutting, laser cutting simulation algorithm) are selected from the preset cutting processing scheme library. For each matching scheme, the cutting tool type (such as virtual circular knife, straight knife), feed speed (0.1-10m / min adjustable), cutting angle (0°-90°, resolution 0.1°) and other process parameters are set in the software, the stress and strain distribution of the fabric during cutting is simulated by the finite element analysis (FEA) module, the friction and shear force data of the contact point between the tool and the fabric are calculated in real time, and K simulated cutting backing fabric three-dimensional models containing fiber fracture trajectory, edge fluff index and other details are generated, each model is attached with corresponding cutting process parameter log and physical property estimation value (such as edge tensile strength retention rate).

[0076] The quality of the K simulated cutting backing fabrics is evaluated by a multi-dimensional quantitative evaluation model: first, a three-level index system is constructed, the first-level index covers edge integrity, dimensional accuracy, material damage degree; the second-level index includes fluff distribution density, heat affected zone width, profile deviation rate and other characteristic parameters; the third-level index extracts quantitative features through computer vision algorithms (such as edge detection, feature point matching) and finite element post-processing techniques (such as stress concentration analysis). After standardizing the index data to the [0, 1] interval, the weight matrix is determined by the analytic hierarchy process, and the quality score of each scheme is calculated by the fuzzy comprehensive evaluation model. For example, when the edge fluff distribution uniformity, heat damage threshold compliance, profile deviation rate and other indicators of a scheme all meet the preset process standards, a higher quality coefficient is output, and finally a quality evaluation vector of the K schemes is formed, providing basic data for subsequent variance analysis.

[0077] The adaptability of the backing fabric to the cutting process is analyzed quantitatively: first, the dispersion degree (variance) of the K quality coefficients is calculated, the smaller the variance, the more stable and consistent the quality performance of different cutting schemes, the better the matching of the backing fabric structure and the current cutting parameters; otherwise, it indicates that the scheme fluctuates greatly and there is a process risk. Take the reciprocal of the variance as the stability index, and normalize it to the [0, 1] interval through nonlinear mapping, the closer the adaptability coefficient generated is to 1, the stronger the processing adaptability of the backing fabric in the current cutting space, which can be used as a key indicator to guide subsequent optimization decisions.

[0078] In one possible implementation, step S410 further includes: Step S411: The lining cloth cutting processing space includes a plurality of lining cloth cutting processing schemes, the lining cloth ironing and shaping space includes a plurality of lining cloth ironing and shaping schemes, and the lining cloth hot melt bonding space includes a plurality of lining cloth hot melt bonding schemes.

[0079] Specifically, each functional module of the lining cloth finishing space contains a diversified process scheme set: the lining cloth cutting processing space integrates multiple cutting process types (such as laser cutting, mechanical cutting, water jet cutting), covers different tool parameters, cutting speeds and path planning strategies, and forms a cutting scheme library covering multiple contour complexities and material characteristics; the lining cloth ironing and shaping space is based on the combination of core process parameters such as temperature, pressure and time to build a scheme set containing multiple shaping modes (such as flat ironing, pressure ironing and rotary ironing) to adapt to the thermal plastic deformation needs of different lining cloth materials; the lining cloth hot melt bonding space designs diversified composite process schemes around parameters such as hot melt adhesive type, gluing method, bonding temperature and pressure to support bonding scenarios with different adhesive fluidity and interfacial bonding strength requirements. The process schemes in each space are realized by parameterized modeling to achieve flexible configuration, forming an extensible process scheme pool to provide a multi-dimensional evaluation basis for simulation detection.

[0080] Embodiment two, based on the same inventive concept as the three-dimensional weaving method of one of the preceding embodiments, as shown in Figure 2 The present application provides a three-dimensional weaving system for lining cloth, and the system and method embodiments in the present application are based on the same inventive concept. The system comprises: A simulated weaving lining cloth acquisition module 10 is configured to simulate weaving according to a three-dimensional weaving scheme for lining cloth to obtain a simulated weaving lining cloth.

[0081] A feature comparison result acquisition module 20 is configured to compare the simulated weaving lining cloth with a design scheme for lining cloth at multiple scales to obtain a feature comparison result for lining cloth.

[0082] A detection result acquisition module 30 is configured to detect the simulated weaving lining cloth for wear risk according to an application correlation layer for lining cloth to obtain a wear risk detection result for lining cloth.

[0083] An adaptive detection result acquisition module 40 is configured to detect the simulated weaving lining cloth for finishing adaptability according to a finishing space for lining cloth to obtain a finishing adaptability detection result for lining cloth.

[0084] A lining cloth weaving optimization strategy acquisition module 50 is configured to optimize parameters of the three-dimensional weaving scheme for lining cloth according to the feature comparison result for lining cloth, the wear risk detection result for lining cloth and the finishing adaptability detection result for lining cloth to obtain a weaving optimization strategy for lining cloth.

[0085] Further, the system is also used to implement the following functions: According to the lining cloth application association layer and the simulated woven lining cloth, a clothing structure model is constructed, the lining cloth application association layer includes a lining cloth association fabric layer and a lining cloth association lining layer; according to the clothing structure model, a collapse risk prediction result of the simulated woven lining cloth is obtained; according to the clothing structure model, an abrasion and peeling risk prediction result of the simulated woven lining cloth is obtained; the lining cloth collapse risk prediction result and the abrasion and peeling risk prediction result are added to the lining cloth clothing risk detection result.

[0086] Further, the system is also used to realize the following functions: The clothing application scene of the clothing structure model is subjected to clothing stress feature mining, and a plurality of clothing stress conditions are constructed; according to the plurality of clothing stress conditions, the clothing structure model is subjected to dynamic simulation respectively, and a plurality of structure deformation simulation data are obtained; according to the plurality of structure deformation simulation data, a set of active compression yield features is obtained through lining cloth active compression yield feature identification; according to the plurality of structure deformation simulation data, a set of passive compression yield features is obtained through lining cloth passive compression yield feature identification; according to the set of active compression yield features and the set of passive compression yield features, a multi-point collapse risk evaluation of the simulated woven lining cloth is performed, and the lining cloth collapse risk prediction result is generated.

[0087] Further, the system is also used to realize the following functions: According to a plurality of clothing actions, the clothing structure model is subjected to dynamic simulation, and a plurality of structure simulation response data are obtained; the plurality of structure simulation response data are input into a lining cloth abrasion risk analysis model, and a plurality of lining cloth abrasion risk coefficients are obtained; the plurality of structure simulation response data are input into a lining cloth peeling risk analysis model, and a plurality of lining cloth peeling risk coefficients are obtained; the plurality of lining cloth abrasion risk coefficients and the plurality of lining cloth peeling risk coefficients are added to the abrasion and peeling risk prediction result.

[0088] Further, the system is also used to realize the following functions: The lining cloth finishing space includes a lining cloth cutting processing space, a lining cloth ironing and shaping space, and a lining cloth hot melt bonding space; according to the lining cloth cutting processing space, a cutting processing adaptability detection of the simulated woven lining cloth is performed, and a lining cloth processing adaptability first coefficient is obtained; according to the lining cloth ironing and shaping space, an ironing and shaping adaptability detection of the simulated woven lining cloth is performed, and a lining cloth processing adaptability second coefficient is obtained; according to the lining cloth hot melt bonding space, a hot melt bonding adaptability detection of the simulated woven lining cloth is performed, and a lining cloth processing adaptability third coefficient is obtained; the lining cloth processing adaptability first coefficient, the lining cloth processing adaptability second coefficient, and the lining cloth processing adaptability third coefficient are added to the lining cloth processing adaptability detection result.

[0089] Further, the system is also used to implement the following functions: According to the lining cloth design scheme, a matching analysis is performed on the lining cloth cutting processing space to obtain K matching cutting processing schemes, K being a positive integer greater than 1; according to the K matching cutting processing schemes, the simulated woven lining cloth is subjected to simulated cutting processing to obtain K simulated cutting lining cloths; cutting processing quality evaluation is performed on the K simulated cutting lining cloths to obtain K cutting processing quality coefficients; and the reciprocal of the variance of the K cutting processing quality coefficients is calculated to generate a first coefficient of the lining cloth processing adaptability.

[0090] Further, the system is also used to implement the following functions: The lining cloth cutting processing space includes a plurality of lining cloth cutting processing schemes, the lining cloth ironing and setting space includes a plurality of lining cloth ironing and setting schemes, and the lining cloth hot melt bonding space includes a plurality of lining cloth hot melt bonding schemes.

[0091] Further, the system is also used to implement the following functions: The simulated woven lining cloth is subjected to multi-scale expansion to obtain a simulated lining cloth micro layer, a simulated lining cloth meso layer and a simulated lining cloth macro layer; according to the lining cloth design scheme, micro mapping comparison is performed on the simulated lining cloth micro layer to obtain a lining cloth micro comparison result; according to the lining cloth design scheme, meso mapping comparison is performed on the simulated lining cloth meso layer to obtain a lining cloth meso comparison result; according to the lining cloth design scheme, macro mapping comparison is performed on the simulated lining cloth macro layer to obtain a lining cloth macro comparison result; and the lining cloth micro comparison result and the lining cloth meso comparison result are combined to generate the lining cloth feature comparison result.

[0092] Embodiment three, based on the same inventive concept as the three-dimensional weaving method of one kind of lining cloth in the foregoing embodiments, the present embodiment provides a computer program product, which can be used to store software programs, computer executable programs and modules, such as the program instructions / modules corresponding to the three-dimensional weaving method of one kind of lining cloth in the embodiments of the present application. The processor executes the software programs, instructions and modules stored in the memory, thereby performing various functional applications and data processing of the computer device, i.e. implementing the above-mentioned three-dimensional weaving method of one kind of lining cloth.

[0093] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. Moreover, the above-mentioned specific embodiments of the present application are described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0094] The above description is only the preferred embodiment of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0095] The specification and drawings are only exemplary and illustrative of the present application and are to be considered within the scope of the present application. Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and equivalent technology, the present application is intended to include these modifications and variations.

Claims

1. A method of volumetric weaving of a lining fabric, characterized in that, The method comprises: According to the lining cloth three-dimensional weaving scheme, simulate weaving to obtain a simulated weaving lining cloth; According to the lining cloth design scheme, perform multi-scale comparison on the simulated weaving lining cloth to obtain a lining cloth characteristic comparison result; According to the lining cloth application correlation layer, perform wearing risk detection on the simulated weaving lining cloth to obtain a lining cloth wearing risk detection result; According to the lining cloth finishing space, perform finishing adaptability detection on the simulated weaving lining cloth to obtain a lining cloth finishing adaptability detection result; According to the lining cloth characteristic comparison result, the lining cloth wearing risk detection result, and the lining cloth finishing adaptability detection result, perform parameter optimization on the lining cloth three-dimensional weaving scheme to obtain a lining cloth weaving optimization strategy.

2. A method of three-dimensional weaving of a lining fabric according to claim 1, characterized in that, According to the lining cloth application correlation layer, perform wearing risk detection on the simulated weaving lining cloth to obtain a lining cloth wearing risk detection result, comprising: According to the lining cloth application correlation layer and the simulated weaving lining cloth, construct a wearing structure model, wherein the lining cloth application correlation layer comprises a lining cloth related fabric layer and a lining cloth related lining layer; According to the wearing structure model, perform collapse risk prediction on the simulated weaving lining cloth to obtain a lining cloth collapse risk prediction result; According to the wearing structure model, perform wear and tear risk prediction on the simulated weaving lining cloth to obtain a wear and tear risk prediction result; Add the lining cloth collapse risk prediction result and the wear and tear risk prediction result to the lining cloth wearing risk detection result.

3. A method of three-dimensional weaving of a lining fabric according to claim 2, characterized in that, According to the wearing structure model, perform collapse risk prediction on the simulated weaving lining cloth to obtain a lining cloth collapse risk prediction result, comprising: Perform wearing stress feature mining on the wearing application scene of the wearing structure model to construct a plurality of wearing stress conditions; According to the plurality of wearing stress conditions, respectively perform dynamic simulation on the wearing structure model to obtain a plurality of structure deformation simulation data; According to the plurality of structure deformation simulation data, perform active compression yield feature identification on the lining cloth to obtain an active compression yield feature set; According to the plurality of structure deformation simulation data, perform passive compression yield feature identification on the lining cloth to obtain a passive compression yield feature set; According to the active compression yield feature set and the passive compression yield feature set, perform multi-point collapse risk evaluation on the simulated weaving lining cloth to generate the lining cloth collapse risk prediction result.

4. A method of three-dimensional weaving of a lining fabric according to claim 2, wherein According to the wearing structure model, perform wear and tear risk prediction on the simulated weaving lining cloth to obtain a wear and tear risk prediction result, comprising: According to a plurality of wearing actions, perform dynamic simulation on the wearing structure model to obtain a plurality of structure simulation response data; Input the plurality of structure simulation response data into a lining cloth wear risk analysis model to obtain a plurality of lining cloth wear risk coefficients; Input the plurality of structure simulation response data into a lining cloth peeling risk analysis model to obtain a plurality of lining cloth peeling risk coefficients; Add the plurality of lining cloth wear risk coefficients and the plurality of lining cloth peeling risk coefficients to the wear and tear risk prediction result.

5. A method of three-dimensional weaving of a lining fabric according to claim 1, wherein According to the lining cloth finishing space, perform finishing adaptability detection on the simulated weaving lining cloth to obtain a lining cloth finishing adaptability detection result, comprising: The lining cloth finishing space includes a lining cloth cutting finishing space, a lining cloth ironing and shaping space, and a lining cloth hot melt bonding space; The lining cloth cutting finishing space is used for cutting finishing adaptability detection on the simulated woven lining cloth, to obtain a first lining cloth finishing adaptability coefficient; The lining cloth ironing and shaping space is used for ironing and shaping adaptability detection on the simulated woven lining cloth, to obtain a second lining cloth finishing adaptability coefficient; The lining cloth hot melt bonding space is used for hot melt bonding adaptability detection on the simulated woven lining cloth, to obtain a third lining cloth finishing adaptability coefficient; The first, second, and third lining cloth finishing adaptability coefficients are added to the lining cloth finishing adaptability detection result.

6. A method of three-dimensional weaving of a lining fabric according to claim 5, characterized in that, The lining cloth cutting finishing space is used for cutting finishing adaptability detection on the simulated woven lining cloth, to obtain a first lining cloth finishing adaptability coefficient, including: The lining cloth design scheme is used for matching analysis on the lining cloth cutting finishing space, to obtain K matching cutting finishing schemes, K being a positive integer greater than 1; The K matching cutting finishing schemes are used for simulated cutting finishing on the simulated woven lining cloth, to obtain K simulated cutting lining cloths; The K simulated cutting lining cloths are subjected to cutting finishing quality evaluation, to obtain K cutting finishing quality coefficients; The reciprocal of the variance of the K cutting finishing quality coefficients is calculated, to generate the first lining cloth finishing adaptability coefficient.

7. A method of three-dimensional weaving of a lining fabric according to claim 5, wherein The lining cloth cutting finishing space includes a plurality of lining cloth cutting finishing schemes, the lining cloth ironing and shaping space includes a plurality of lining cloth ironing and shaping schemes, and the lining cloth hot melt bonding space includes a plurality of lining cloth hot melt bonding schemes.

8. A method of three-dimensional weaving of a lining fabric as claimed in claim 1, characterized in that, The lining cloth design scheme is used for multi-scale comparison on the simulated woven lining cloth, to obtain a lining cloth feature comparison result, including: The simulated woven lining cloth is subjected to multi-scale expansion, to obtain a simulated lining cloth micro layer, a simulated lining cloth meso layer, and a simulated lining cloth macro layer; The lining cloth design scheme is used for micro mapping comparison on the simulated lining cloth micro layer, to obtain a lining cloth micro comparison result; The lining cloth design scheme is used for meso mapping comparison on the simulated lining cloth meso layer, to obtain a lining cloth meso comparison result; The lining cloth design scheme is used for macro mapping comparison on the simulated lining cloth macro layer, to obtain a lining cloth macro comparison result, and the lining cloth feature comparison result is generated by combining the lining cloth micro comparison result and the lining cloth meso comparison result.

9. A three-dimensional weaving system for a lining cloth, characterized by The system is used for implementing the lining cloth three-dimensional weaving method of any one of claims 1-8, and the system includes: A simulated woven lining cloth acquisition module is configured to simulate weaving according to a lining cloth three-dimensional weaving scheme to obtain a simulated woven lining cloth; A feature comparison result acquisition module is configured to perform multi-scale comparison on the simulated woven lining cloth according to a lining cloth design scheme to obtain a lining cloth feature comparison result; A detection result acquisition module is configured to perform wearing risk detection on the simulated woven lining cloth according to a lining cloth application correlation layer to obtain a lining cloth wearing risk detection result; An adaptive detection result acquisition module is configured to perform adaptive detection on the analog woven interlining according to interlining finishing space to obtain interlining finishing adaptability detection results; An interlining weaving optimization strategy acquisition module is configured to perform parameter optimization on the interlining three-dimensional weaving scheme according to the interlining feature comparison results, the interlining wear risk detection results and the interlining finishing adaptability detection results to obtain an interlining weaving optimization strategy.

10. A computer program product having stored thereon a computer program, characterized in that, The computer program is executed by a processor to implement the steps in the interlining three-dimensional weaving method according to any one of claims 1-8.

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