A method for sewing suit fabrics based on structural hierarchical analysis

Abstract

The present application provides a method for sewing suit fabrics under structural hierarchical analysis, which relates to the technical field of clothing manufacturing processes, including: cutting and overlapping according to a preset sewing scheme to form an initial structure; using a 3D visual collector to perform three-dimensional simulation to generate an edge position set; planning the sewing path; using an adaptive tension control system to monitor the tension in real time, and adaptively adjust the cloth feeding speed and stitch strength of the sewing machine; and performing edge sewing on the initial structure according to the sewing path, cloth feeding speed, and stitch strength through the sewing system. The present application can solve the technical problem in the prior art that elastic fabrics are easily stretched and deformed during the sewing process, and the sewing line becomes uneven, which leads to the displacement of the sewing position. Cutting is performed according to a preset scheme, and three-dimensional simulation is performed using a 3D visual collector to accurately determine the edge position, thereby improving the accuracy of the sewing position and thus improving the sewing quality and efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of clothing manufacturing technology, and in particular to a method for sewing suit fabrics under structural level analysis. Background Art

[0002] In the field of textile and clothing manufacturing, elastic fabrics are widely used due to their good elasticity and comfort. In the sewing process, due to their special physical properties, a series of technical difficulties are easily encountered. Elastic fabrics are prone to tensile deformation when subjected to external forces, making it difficult to maintain the stability and consistency of the fabric during the sewing process. Traditional sewing methods are difficult to accurately control the position and tension of the suture line, resulting in uneven suture lines, suture position deviation and other problems, which seriously affect the quality and appearance of the product. In response to technical difficulties, existing technologies have made some attempts and improvements. Some advanced sewing equipment has introduced a tension control system, trying to reduce the tensile deformation during the sewing process by adjusting the tension. However, there are problems such as complex operation and high cost, and the effect is not ideal in practical applications. In addition, some special sewing materials and processes are used to improve the firmness and aesthetics of the suture line, but they cannot fundamentally solve the problems of tensile deformation and uneven suture lines during the sewing process of elastic fabrics.

[0003] In summary, the prior art has a technical problem in that the elastic fabric is easily stretched and deformed during the sewing process, resulting in uneven sewing lines and thus causing the sewing position to shift. Summary of the invention

[0004] The purpose of this application is to provide a method for sewing suit fabrics under structural hierarchical analysis, so as to solve the technical problem in the prior art that elastic fabrics are easily stretched and deformed during the sewing process, resulting in uneven sewing lines and thus causing sewing position deviation.

[0005] In view of the above problems, the present application provides a method for sewing suit fabrics under structural hierarchical analysis, wherein the method for sewing suit fabrics under structural hierarchical analysis comprises: cutting and overlapping multiple layers of suit fabrics according to a preset sewing scheme to form an initial structure; using a 3D vision collector to perform three-dimensional simulation on the edges of the multiple layers of suit fabrics to generate an edge position set; transmitting the edge position set to a control module of a sewing machine, and planning a sewing path based on the control module; using an adaptive tension control system to monitor the tension of the multiple layers of suit fabrics in real time, and adaptively adjusting the cloth feeding speed and stitch strength of the sewing machine according to the tension; performing edge sewing on the initial structure through a sewing system according to the sewing path, the cloth feeding speed and the stitch strength.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The initial structure is formed by cutting and overlapping the multi-layer suit fabric according to the preset stitching scheme; the edge of the multi-layer suit fabric is simulated in three dimensions by using a 3D vision collector to generate an edge position set; the edge position set is transmitted to the control module of the stitching machine, and the stitching path is planned based on the control module; the tension of the multi-layer suit fabric is monitored in real time by using an adaptive tension control system, and the cloth feeding speed and stitch strength of the stitching machine are adaptively adjusted according to the tension; the edge stitching of the initial structure is performed by the stitching system according to the stitching path, the cloth feeding speed and the stitch strength. In other words, cutting is performed according to the preset scheme, and the 3D vision collector is used to perform three-dimensional simulation to accurately determine the edge position, thereby improving the accuracy of the stitching position, thereby improving the stitching quality and efficiency.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0010] Figure 1 A schematic diagram of a process of sewing a suit fabric under a structural level analysis of the present application;

[0011] Figure 2 This is a schematic diagram of the process of obtaining the cloth feeding speed and stitch force in a suit cloth sewing method under a structural level analysis of the present application. DETAILED DESCRIPTION

[0012] This application provides a method and system for sewing suit fabrics under structural hierarchical analysis, which solves the technical problem in the prior art that elastic fabrics are easily stretched and deformed during the sewing process, resulting in uneven sewing lines and thus offset sewing positions. Cutting is performed through a preset plan, and a 3D visual collector is used for three-dimensional simulation to accurately determine the edge position, thereby improving the accuracy of the sewing position and thus improving the sewing quality and efficiency.

[0013] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0014] For examples, please see the attached Figure 1 The present application provides a method for sewing suit fabrics under structural hierarchical analysis, wherein the method for sewing suit fabrics under structural hierarchical analysis specifically comprises the following steps:

[0015] Step 1: Cut and overlap multiple layers of suit fabric according to the preset stitching plan to form an initial structure.

[0016] Specifically, the preset stitching scheme is the combination of fabrics, cutting methods, stitching methods, overlapping order, etc. determined during the design stage. According to the preset scheme, select appropriate fabrics, including outer fabrics, linings, and interlinings, etc., mark and cut the fabrics according to the suit pattern, and preliminarily overlap the cut outer fabrics, linings, and interlinings according to the design scheme. Ensure that the positions of each layer of fabric are accurate and the overlapping parts meet the design requirements. Multiple layers of fabric are combined and fixed together to form the initial structure of the suit. The preset stitching scheme ensures the precise docking of multiple layers of fabric during the cutting and overlapping process.

[0017] Step 2: Use a 3D vision collector to perform three-dimensional simulation on the edge of the multi-layer suit fabric to generate an edge position set.

[0018] Specifically, a 3D vision collector is used to scan the edges of multi-layer suit fabrics to generate a three-dimensional image. The 3D vision collector is an advanced sensor device that is mainly used to capture and process object data in three-dimensional space, provide accurate edge position and shape information, and can adapt to fabrics of different colors, textures and materials, improving the accuracy and reliability of detection. The three-dimensional image is analyzed using an image processing algorithm, and computer vision technologies such as edge detection and feature extraction are used to identify the edge of the fabric and separate key edge information from complex image data. Due to the influence of various noises during 3D scanning and image processing, the initial edge position set contains inaccurate or erroneous data points. The edge position is denoised and distorted to ensure the accuracy of the edge position data and obtain a more accurate and cleaner edge position set. The edge information of the fabric is accurately obtained through high-tech means, and the accurate edge position data directly affects the stitching quality and the overall appearance of the suit.

[0019] Step three: transmitting the edge position set to a control module of a stitching machine, and planning a stitching path based on the control module.

[0020] Specifically, the edge position set is transmitted to the control module of the sewing machine for comparison and matching with the preset sewing scheme. The preset sewing scheme is a pre-set sewing path based on design requirements and fabric characteristics. Based on the matching results, the control module generates an initial sewing path to instruct the sewing machine how to move the needle and fabric to complete the sewing process. The deformation degree of the fabric under stretching, compression and twisting is measured, and the deformation data of the fabric under various forces are collected to understand the behavior and reaction of the fabric under different conditions. The deformation data is input into the convolutional neural network for training, and the various characteristics of fabric deformation are learned through training to predict the deformation of the fabric under different conditions. The data of the multi-layer suit fabric is input into the trained deformation prediction model, including the material, thickness, elasticity and other characteristics of the fabric. The deformation prediction model analyzes the input data, identifies and predicts the areas where the fabric may deform during the sewing process, which are caused by the elasticity of the fabric or the stress concentration during the sewing process. Based on the predicted deformation area, the initial sewing path is optimized and the sewing path is adjusted to reduce the impact of deformation on the sewing quality, ensuring the accuracy of sewing and the overall beauty of the suit. The control module optimizes the sewing path to ensure that the sewing path conforms to the preset plan and adapts to the actual deformation of the fabric, thereby improving the accuracy and quality of sewing.

[0021] Step 4: Use an adaptive tension control system to monitor the tension of the multi-layer suit fabric in real time, and adaptively adjust the fabric feeding speed and stitch strength of the sewing machine according to the tension.

[0022] Specifically, the tension control system monitors the tension of the fabric in real time through sensors. The adaptive tension control system is a high-tech control system that can monitor and adjust the tension of the fabric during the sewing process in real time, and usually includes components such as sensors, controllers, and actuators. These sensors are located in different parts of the sewing machine to fully capture the tension changes of the fabric. Based on the monitored tension data, the control system automatically adjusts the feed speed and stitch strength of the sewing machine. For example, if the tension of the fabric is too large, the system will slow down the feed speed or reduce the stitch strength to avoid tearing or excessive deformation of the fabric. The feed speed and stitch strength are evaluated for adaptability to assess whether the current speed and strength match the elastic properties of the fabric to ensure that the fabric will not be damaged by improper tension during the sewing process. Based on the results of the adaptability evaluation, the feed speed and stitch strength are locally optimized to determine the most suitable feed speed and stitch strength. The feed speed and stitch strength are fine-tuned according to factors such as the thickness and material of the fabric. For example, for thicker fabrics, the system may slow down the feed speed or increase the stitch strength to ensure the firmness of the stitching. The adaptive tension control system enables the sewing system to adapt to fabrics of different materials and characteristics, ensuring the best sewing results in all situations.

[0023] Step 5: Perform edge stitching on the initial structure through a stitching system according to the stitching path, the cloth feeding speed and the stitch strength.

[0024] Specifically, the initial structure is evaluated for overlap stability, and the stability of the fabric in the overlap area is assessed. If the overlap stability coefficient does not meet the preset overlap stability threshold, that is, the fabric is not stable enough in the overlap area, the initial structure is adjusted by the adaptive clamping device. The adaptive clamping device automatically adjusts the clamping force and position according to the actual state and stability requirements of the fabric to enhance the stability of the fabric in the overlap area and ensure the smooth progress of the sewing process. If the overlap stability coefficient meets the preset overlap stability threshold, that is, the fabric has good stability in the overlap area, the sewing system performs edge sewing based on the sewing path, feed speed and stitch strength. It ensures that the fabric structure has sufficient stability before sewing begins, thereby avoiding displacement or deformation during the sewing process, improving the accuracy of sewing and the quality of the finished product.

[0025] Further, step 2 of this application includes:

[0026] The 3D vision collector is used to scan the edge of the multi-layer suit fabric to generate a three-dimensional image; the fabric edge features of the three-dimensional image are identified and extracted by an image processing algorithm to obtain an initial edge position set; the initial edge position set is denoised and distortion corrected to generate an edge position set.

[0027] Specifically, the multi-layered suit fabric is placed on a suitable workbench to ensure that the fabric is flat and wrinkle-free. The edge of the multi-layered fabric is scanned using a 3D vision collector to generate an accurate three-dimensional image. The collector emits light (such as a laser) to the surface of the fabric and calculates the three-dimensional coordinates of the edge of the fabric by receiving the reflected light. The 3D vision collector is a high-precision scanning device used to capture the three-dimensional data of an object. Through the image processing algorithm, the features of the edge of the fabric are identified and extracted from the three-dimensional image, including the edge contour, corners, curves, etc., to generate an initial edge position set, which contains the three-dimensional coordinate information of each point on the edge of the fabric. Noise points introduced by scanning or environmental factors are identified and removed, and smoothing algorithms such as Gaussian filtering and median filtering are applied to reduce the impact of noise on the edge position data. The initial edge position set is analyzed to detect possible distortions, such as distortion caused by bending, folding of the fabric, or lens distortion of the scanning device itself. The correction algorithm is applied to correct the detected distortion to ensure the accuracy of the edge position data. The initial edge position set is optimized by combining the results of denoising and distortion correction to generate the final edge position set. Through denoising and distortion correction, the edge data of multi-layer suit fabrics is ensured to be accurate and reliable.

[0028] Further, step three of this application includes:

[0029] The control module matches the edge position set with the preset stitching scheme to generate an initial stitching path; based on the elastic properties of the fabric, the deformation data of the multi-layer suit fabric is collected to establish a deformation feature database; a convolutional neural network is used to learn the deformation feature database to generate a deformation prediction model; the multi-layer suit fabric is input into the deformation prediction model to identify the predicted deformation area, and the initial stitching path is optimized according to the predicted deformation area to obtain the stitching path.

[0030] Specifically, the edge position set generated by the 3D vision collector is input into the control module of the sewing machine. The control module is the core control unit of the sewing machine, which is responsible for processing data and controlling the operation of the sewing machine. The edge position set is matched with the preset sewing scheme to generate the initial sewing path. The elastic properties of the fabric are analyzed, and the deformation data of the fabric under tension and pressure are collected, including the degree of deformation, deformation direction and deformation area. The collected deformation data are sorted and a deformation feature database is established. The deformation feature database is learned using a convolutional neural network to generate a deformation prediction model. According to the input new data, the deformation of the fabric under different conditions is predicted. Convolutional neural network is a deep learning algorithm that is particularly suitable for processing image and vision-related tasks. CNN can effectively identify and learn features in images by simulating the visual processing mechanism of the human brain. The database is divided into training set, validation set and test set, and the model is trained, verified and tested to ensure the accuracy of the model prediction. The input data of the multi-layer suit fabric is passed to the deformation prediction model to identify and predict the deformation area of ​​the fabric during the sewing process. According to the predicted deformation area, the initial sewing path is optimized to adapt to the actual deformation of the cloth, so as to obtain a better sewing path. The optimization adjustment includes changing the sewing order, the curvature of the path or the sewing parameters. By introducing the deformation prediction model, the sewing system can adapt to cloths of different materials and characteristics, ensuring the best sewing effect in various situations.

[0031] Further, step 4 of this application includes:

[0032] A tension sensor based on a sewing machine monitors the tension changes of the multi-layer suit fabric in real time to generate tension data; based on the tension data, the tension adjustment parameters of the sewing machine are adaptively adjusted, and an adaptability evaluation based on the elastic characteristics of the fabric is performed, and the tension adjustment parameters are dynamically optimized according to the adaptability of the fabric; based on the tension adjustment parameters, local optimization is performed to obtain the cloth feeding speed and stitch strength.

[0033] Specifically, a tension sensor is installed on the sewing machine to monitor the tension changes of the fabric during the sewing process in real time. The tension data is divided into two parts, namely the first tension parameter and the second tension parameter, which represent different sewing stages or fabric areas. The adaptability of the first tension parameter and the second tension parameter is evaluated according to the elastic characteristics, and the matching degree between the extracted tension parameter and the elastic characteristics of the fabric is evaluated to determine whether the tension of the fabric is suitable for the sewing process. The fitness of the two tension parameters is merged within the iteration cycle to obtain a comprehensive parameter that reflects the comprehensive tension characteristics during the sewing process. The tension data is traversed, and the tension adjustment parameter is optimized using the first merged tension parameter.

[0034] According to the characteristics of different fabric areas (such as thickness, elasticity, etc.), local optimization is performed to determine the optimal feed speed and stitch strength. By combining the tension adjustment parameters with the fabric deformation and stitch uniformity indicators, the initial feed speed and stitch strength suitable for the current fabric state are generated. The adaptability of the initial feed speed and initial stitch strength is evaluated to obtain the initial feed speed adaptability and initial stitch strength adaptability. The applicability and effect of the initial parameters in the actual sewing process are evaluated to determine their adaptability. By analyzing the relationship between the feed speed and stitch strength, the balance point between the two is determined to ensure the stability and consistency of the sewing process. By introducing a competition mechanism and a local search strategy, the relationship between the feed speed and stitch strength is optimized to find the optimal balance point. Comprehensively considering the adaptability of the feed speed and stitch strength, the final feed speed and stitch strength are determined to ensure that the deformation of the fabric can be adapted in the actual sewing process, the accuracy of the sewing position and the uniformity of the sewing line can be maintained. Through real-time monitoring and adaptive adjustment, the problem of tension change of elastic fabrics in the sewing process is effectively solved, thereby further improving the overall quality and production efficiency of suits.

[0035] Furthermore, the present application also includes the following steps:

[0036] A first tension parameter and a second tension parameter are extracted based on the tension data; the first tension parameter and the second tension parameter are adaptably evaluated according to the elastic characteristics of the fabric to obtain a first tension adaptability and a second tension adaptability; based on a preset iteration cycle, tension is merged according to the first tension adaptability and the second tension adaptability to obtain a first merged tension parameter; the tension data is traversed according to the first merged tension parameter to obtain the tension adjustment parameter.

[0037] Specifically, the data collected by the tension sensor is analyzed, and the complex tension changes are decomposed into two parts, namely the first tension parameter and the second tension parameter, which represent different stitching stages or fabric areas. An evaluation model is established according to the elastic properties of the fabric to evaluate the adaptability of the parameters. The first tension parameter and the second tension parameter are input into the evaluation model for adaptability evaluation to obtain the first tension fitness and the second tension fitness, and determine the fitness of each parameter, that is, its applicability and effect under the current fabric state. An iteration cycle is pre-set, and the tension fitness is merged during this cycle. During the iteration cycle, a certain merging strategy (such as weighted average, optimal selection, etc.) is adopted according to the first tension fitness and the second tension fitness to obtain the first merged tension parameter. This parameter combines the effects of the two original parameters and can better reflect the actual tension state of the fabric. The first merged tension parameter is used to traverse the entire tension data set, and each data point is adjusted according to the merged tension parameter to find the tension adjustment parameter that is most suitable for the entire stitching process, ensuring that the stitching machine can maintain the best tension state for different fabrics and different stitching stages. By introducing data-driven and using fabric elastic characteristics for adaptability evaluation, the adaptability of the sewing path and process is enhanced, and the control ability of the cloth tension characteristics is improved through iterative and merging processing.

[0038] Further, as attached Figure 2 As shown, the application also includes the following steps:

[0039] The index is split according to the elastic characteristics of the fabric to obtain the fabric deformation and stitch uniformity; the tension adjustment parameters are split based on the fabric deformation and stitch uniformity respectively to obtain the initial cloth feed speed and the initial stitch strength; the adaptability of the initial cloth feed speed and the initial stitch strength is evaluated to obtain the adaptability of the initial cloth feed speed and the adaptability of the initial stitch strength; the balance relationship between the cloth feed speed and the stitch strength is generated according to the adaptability of the initial cloth feed speed and the adaptability of the initial stitch strength; an enhanced competition mechanism is introduced, and the balance relationship is optimized using a local search strategy to obtain the adaptability of the cloth feed speed and the adaptability of the stitch strength; the cloth feed speed and the stitch strength are obtained based on the adaptability of the cloth feed speed and the adaptability of the stitch strength.

[0040] Specifically, the elastic properties of the fabric are analyzed, and the key indicators in the sewing process are divided into two parts: fabric deformation and stitch uniformity. Fabric deformation reflects the degree of deformation of the fabric under tension, and stitch uniformity is used to measure the uniformity of the stitches during the sewing process. The tension adjustment parameters are combined with the fabric deformation and stitch uniformity indicators to generate the initial feed speed and stitch strength suitable for the current fabric state. The initial feed speed refers to the feed speed of the fabric at the beginning of sewing, which affects the efficiency and quality of sewing and ensures that the fabric will not be excessively deformed during the sewing process due to improper feed speed. The initial stitch strength refers to the strength applied by the stitches at the beginning of sewing, which ensures the uniformity and firmness of the stitches during sewing. The adaptability of the initial feed speed is evaluated to obtain the adaptability of the initial feed speed to evaluate its effectiveness in actual operation. The adaptability of the initial stitch strength is evaluated to obtain the adaptability of the initial stitch strength to evaluate its applicability in the sewing process.

[0041] The adaptability of the initial feed speed and the adaptability of the initial stitch force are analyzed to determine the optimal balance between the two. The balance relationship refers to a balance state that needs to be achieved between the feed speed and the stitch force during the sewing process to ensure the quality of the sewing. The enhanced competition mechanism is introduced to promote the rapid convergence and optimization of parameters. The local search strategy is used to search for the optimal solution within a certain range to optimize the balance relationship between the feed speed and the stitch force. Through simulated annealing, genetic algorithm or other optimization algorithms, the feed speed and stitch force are continuously adjusted to achieve the best adaptability. Through the optimization process, the feed speed adaptability and stitch force adaptability are obtained, which is the current optimal choice. By comprehensively considering the feed speed adaptability and stitch force adaptability, the feed speed and stitch force that are most suitable for the current sewing conditions can be determined. Ensure that the deformation of the fabric can be adapted during the actual sewing process, maintain the accuracy of the sewing position and the uniformity of the sewing line. Through fine parameter control and optimization, the deformation and tension change problems of elastic fabrics during the sewing process are effectively solved, thereby further improving the overall quality and production efficiency of suits.

[0042] Furthermore, step five of this application includes:

[0043] An overlapping stability evaluation is performed based on the initial structure to generate an overlapping stability coefficient; if the overlapping stability coefficient does not meet a preset overlapping stability threshold, the initial structure is adjusted by an adaptive clamping device; if the overlapping stability coefficient meets the preset overlapping stability threshold, edge stitching based on the stitching path, the cloth feeding speed and the stitch force is performed by the stitching system.

[0044] Specifically, the initial structure is evaluated for overlap stability, and the overlapped part is evaluated for stability by analyzing factors such as the material, number of layers, and interlayer adhesion of the fabric, including mechanical tests such as stretching and compression of the fabric. Based on the evaluation results, the overlap stability coefficient is calculated by a specific algorithm or model. The overlap stability coefficient is a quantitative indicator used to indicate the degree of stability of the overlapped part. A overlap stability threshold is set in advance to determine whether the fabric structure is stable. The generated overlap stability coefficient is compared with the preset overlap stability threshold. If the overlap stability coefficient does not meet the preset threshold, it indicates that the fabric structure is unstable. The adaptive clamping device is a clamping device that can automatically adjust according to the characteristics of the fabric. The adaptive clamping device is started to adjust the initial structure, including repositioning the fabric layer, increasing the clamping force, or changing the clamping point to enhance the stability of the structure. After the adjustment, the overlap stability evaluation is performed again until the overlap stability coefficient meets the preset threshold. If the overlap stability coefficient meets the preset overlap stability threshold, it indicates that the fabric structure is stable and sewing can be started. Edge sewing is performed according to the previously planned sewing path, combined with the feed speed and stitch strength. By adjusting the initial structure when the overlapping stability coefficient does not meet the threshold, the stability of the sewing process is ensured, and an adaptive clamping device is used to enable the sewing system to adapt to fabrics of different materials and characteristics.

[0045] Furthermore, the present application also includes the following steps:

[0046] Based on the suturing feedback system, edge suturing parameters are monitored in real time, suturing quality evaluation is performed on the edge suturing parameters, and a suturing quality evaluation coefficient is generated; and suturing path planning is performed based on the suturing quality evaluation coefficient.

[0047] Specifically, the suture feedback system is started, which is used to monitor key parameters in the suture process in real time, such as suture stitches, suture strength, suture speed, etc. Based on the collected data, the suture quality is evaluated through a specific algorithm or model, including the evaluation of factors such as the uniformity of the suture stitches and the stability of the suture strength. The suture quality is quantitatively evaluated to determine whether the suture meets the preset quality standards and generate a suture quality evaluation coefficient. The suture quality evaluation coefficient is analyzed to identify possible problem areas or places where parameters are improperly set. Based on the analysis results of the evaluation coefficient, the suture path is dynamically adjusted, including changing the suture order, adjusting the feed speed and stitch strength, and optimizing the thread tension. Through self-monitoring and self-optimization, the stability of the suture process is ensured, and production efficiency and product quality are improved.

[0048] In summary, the suit fabric sewing method under the structural level analysis provided by the present application has the following technical effects:

[0049] The initial structure is formed by cutting and overlapping the multi-layer suit fabric according to the preset stitching scheme; the edge of the multi-layer suit fabric is simulated in three dimensions by using a 3D vision collector to generate an edge position set; the edge position set is transmitted to the control module of the stitching machine, and the stitching path is planned based on the control module; the tension of the multi-layer suit fabric is monitored in real time by using an adaptive tension control system, and the cloth feeding speed and stitch strength of the stitching machine are adaptively adjusted according to the tension; the edge stitching of the initial structure is performed by the stitching system according to the stitching path, the cloth feeding speed and the stitch strength. In other words, cutting is performed according to the preset scheme, and the 3D vision collector is used to perform three-dimensional simulation to accurately determine the edge position, thereby improving the accuracy of the stitching position, thereby improving the stitching quality and efficiency.

[0050] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0051] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. A method for sewing suit fabrics under structural hierarchical analysis, characterized in that: include: Cut and overlap multiple layers of suit fabric according to a preset stitching scheme to form an initial structure; Using a 3D vision collector to perform three-dimensional simulation on the edge of the multi-layer suit fabric to generate an edge position set; Transmitting the edge position set to a control module of a stitching machine, and planning a stitching path based on the control module; Using an adaptive tension control system to monitor the tension of the multi-layer suit fabric in real time, and adaptively adjusting the fabric feeding speed and stitch strength of the sewing machine according to the tension; Performing edge stitching on the initial structure by a stitching system according to the stitching path, the cloth feeding speed and the stitch force; The step of transmitting the edge position set to a control module of a stitching machine and planning a stitching path based on the control module comprises: The control module matches the edge position set with the preset stitching scheme to generate an initial stitching path; Based on the elastic properties of the fabric, deformation data of the multi-layer suit fabric is collected to establish a deformation feature database; Using a convolutional neural network to learn the deformation feature database to generate a deformation prediction model; The deformation data of the multi-layer suit fabric is input into the deformation prediction model to identify the predicted deformation area, and the initial sewing path is optimized according to the predicted deformation area to obtain the sewing path.

2. The method for sewing suit fabrics under structural hierarchical analysis according to claim 1, characterized in that: The method of using a 3D vision collector to perform three-dimensional simulation on the edge of the multi-layer suit fabric to generate an edge position set includes: Scan the edge of the multi-layer suit fabric using the 3D vision collector to generate a three-dimensional image; Identify and extract fabric edge features of the three-dimensional image through an image processing algorithm to obtain an initial edge position set; De-noising and distortion correction are performed on the initial edge position set to generate an edge position set.

3. The method for sewing suit fabrics under structural hierarchical analysis according to claim 1, characterized in that: The method of using an adaptive tension control system to monitor the tension of the multi-layer suit fabric in real time and adaptively adjusting the fabric feeding speed and stitch strength of the sewing machine according to the tension includes: The tension sensor of the sewing machine monitors the tension change of the multi-layer suit fabric in real time and generates tension data; Adaptively adjusting the tension adjustment parameters of the sewing machine according to the tension data, and performing adaptability evaluation based on the elastic properties of the fabric, and dynamically optimizing the tension adjustment parameters according to the adaptability of the fabric; The cloth feeding speed and stitch force are obtained by performing local optimization based on the tension adjustment parameters.

4. The method for sewing suit fabrics under structural hierarchical analysis as claimed in claim 3, characterized in that: The method of adaptively adjusting the tension adjustment parameters of the sewing machine according to the tension data, performing adaptability evaluation based on the elastic characteristics of the fabric, and dynamically optimizing the tension adjustment parameters according to the adaptability of the fabric includes: extracting a first tension parameter and a second tension parameter based on the tension data; Performing adaptability evaluation on the first tension parameter and the second tension parameter according to the elastic properties of the fabric to obtain a first tension adaptability and a second tension adaptability; Based on a preset iteration cycle, performing tension merging according to the first tension adaptability and the second tension adaptability to obtain a first combined tension parameter; The tension data is traversed according to the first combined tension parameter to obtain the tension adjustment parameter.

5. The method for sewing suit fabrics under structural hierarchical analysis as claimed in claim 3, characterized in that: The locally optimizing the tension adjustment parameters to obtain the cloth feeding speed and stitch strength includes: Split the index according to the elastic properties of the fabric to obtain the fabric deformation and stitch uniformity; Splitting the tension adjustment parameters based on the cloth deformation and stitch uniformity respectively to obtain an initial cloth feeding speed and an initial stitch force; The adaptability of the initial cloth feeding speed and the initial stitch force is evaluated to obtain the adaptability of the initial cloth feeding speed and the adaptability of the initial stitch force; Generating a balance relationship between the cloth feeding speed and the stitch force according to the initial cloth feeding speed adaptability and the initial stitch force adaptability; An enhanced competition mechanism is introduced, and the balance relationship is optimized using a local search strategy to obtain the adaptability of the cloth feeding speed and the adaptability of the stitch force. The cloth feed speed and stitch force are obtained based on the cloth feed speed adaptability and stitch force adaptability.

6. The method for sewing suit fabrics under structural hierarchical analysis according to claim 1, characterized in that: The method of performing edge stitching on the initial structure by a stitching system according to the stitching path, the cloth feeding speed and the stitch strength includes: Performing an overlap stability evaluation based on the initial structure to generate an overlap stability coefficient; If the overlapping stability coefficient does not meet the preset overlapping stability threshold, the initial structure is adjusted by an adaptive clamping device; if the overlapping stability coefficient meets the preset overlapping stability threshold, edge stitching based on the stitching path, the feed speed and the stitch force is performed by the stitching system.

7. The method for sewing suit fabrics under structural hierarchical analysis according to claim 1, characterized in that: Also includes: Based on the suturing feedback system, the edge suturing parameters are monitored in real time, the suturing quality of the edge suturing parameters is evaluated, and a suturing quality evaluation coefficient is generated; Suturing path planning is performed based on the suture quality evaluation coefficient.

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