A method for optimizing the bonding between shirt base material and adhesive strip

Abstract

The present application provides a method for optimizing the bonding between shirt base material and adhesive strips, and relates to the technical field of clothing production. The method includes: generating properties of shirt base material at multiple positions; calling an adhesive strip formula mapping library, mapping the properties of shirt base material at multiple positions to adhesive strip formulas, and generating multiple local adhesive strip formulas; constructing multiple local bonding process spaces based on the multiple local adhesive strip formulas, wherein any local bonding process space contains multiple local bonding process composite parameters; calling an adhesive adaptation evaluation function, optimizing the local bonding process in combination with the multiple local bonding process spaces, and generating multiple local optimal bonding process composite parameters; and controlling the bonding of the first shirt base material with the multiple local adhesive strip formulas and the multiple local optimal bonding process composite parameters. The method realizes local precise control of the bonding between the shirt base material and the adhesive strip, and achieves the technical effect of ensuring the bonding quality at the local position and improving the quality of shirt production.

Description

Technical Field

[0001] The present application relates to the technical field of clothing manufacturing, and in particular to a method for optimizing the bonding of shirt base material and adhesive strips. Background Art

[0002] In the shirt manufacturing process, the bonding of the base material and the adhesive strip is a key process step, which directly affects the quality and durability of the shirt. Traditional bonding methods usually use fixed adhesive strip formulas and bonding processes. However, the properties of the base material at different locations of the same shirt base material may be different, resulting in inconsistent local bonding effects and poor bonding quality.

[0003] In summary, the prior art has a technical problem in that the lack of zoning optimization for the base material at different local positions leads to inconsistent local bonding effects, further affecting the production quality of the shirt. Summary of the invention

[0004] The purpose of the present application is to provide a method for optimizing the bonding between a shirt base material and an adhesive strip, so as to solve the technical problem in the prior art that the local bonding effect is inconsistent due to the lack of zoning optimization of the base material at different local positions, which further affects the quality of shirt production.

[0005] In view of the above problems, the present application provides a method for optimizing the bonding of a shirt base material and an adhesive strip, the method comprising: performing multi-position base material analysis and clustering on a first shirt base material in a preset bonding operation position to generate multi-position shirt base material properties; calling an adhesive strip formula mapping library to perform adhesive strip formula mapping on the multi-position shirt base material properties to generate a plurality of local adhesive strip formulas; constructing a plurality of local bonding process spaces based on the plurality of local adhesive strip formulas, wherein any local bonding process space includes a plurality of local bonding process composite parameters; calling an adhesive adaptation evaluation function to perform local bonding process optimization in combination with the plurality of local bonding process spaces to generate a plurality of local optimal bonding process composite parameters; and controlling the bonding of the first shirt base material with the plurality of local adhesive strip formulas and the plurality of local optimal bonding process composite parameters.

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

[0007] 1. Perform multi-position base material analysis and clustering on the first shirt base material in the preset bonding operation position to generate multi-position shirt base material properties; call the adhesive strip formula mapping library to perform adhesive strip formula mapping on the multi-position shirt base material properties to generate multiple local adhesive strip formulas; construct multiple local bonding process spaces based on the multiple local adhesive strip formulas, wherein any local bonding process space contains multiple local bonding process composite parameters; call the bonding adaptation evaluation function, combine the multiple local bonding process spaces to optimize the local bonding process, and generate multiple local optimal bonding process composite parameters; use the multiple local adhesive strip formulas and the multiple local optimal bonding process composite parameters to control the bonding of the first shirt base material. Through multi-position base material analysis and clustering, multiple local adhesive strip formulas can be generated for different shirt base materials, and multiple local bonding process spaces can be constructed to optimize the bonding process at local positions, thereby achieving local precise control of the bonding between the shirt base material and the adhesive strip, thereby achieving the technical effect of ensuring the bonding quality at local positions and improving the quality of shirt production.

[0008] 2. An image monitoring module and a spectrum analysis module are configured at a preset bonding operation position. When the image monitoring module outputs a shirt base material replacement signal, the spectrum analysis module is activated to perform multi-position base material component detection on the second shirt base material replaced in the preset bonding operation position, and output multi-position base material component information; multi-position base material property analysis is performed based on the multi-position base material component information, and the analysis result is compared with the multi-position shirt base material property to determine whether to generate a bonding parameter continuous execution signal. Based on the determination result, bonding optimization is performed on the replaced shirt base material, thereby ensuring that the shirt manufacturing process can flexibly respond to changes brought about by base material replacement, achieving the technical effect of ensuring the continuity and stability of the bonding process and improving the adaptability and efficiency of the production process.

[0009] 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

[0010] 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.

[0011] Figure 1 A schematic diagram of a process for optimizing the bonding between a shirt base material and an adhesive strip according to the present application;

[0012] Figure 2 A schematic diagram of a process for constructing multiple local bonding process spaces in a bonding optimization method for a shirt base material and an adhesive strip according to the present application. DETAILED DESCRIPTION

[0013] This application provides a method for optimizing the bonding between shirt base material and adhesive strips, which solves the technical problem in the prior art that the local bonding effect is inconsistent due to the lack of zoning optimization for base materials at different local positions, further affecting the quality of shirt production. Through multi-position base material analysis and clustering, multiple local adhesive strip formulas can be generated for different shirt base materials, and multiple local bonding process spaces can be constructed to optimize the bonding process at local positions, thereby achieving local precise control of the bonding between shirt base material and adhesive strips, thereby achieving the technical effect of ensuring the bonding quality at local positions and improving the quality of shirt production.

[0014] 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.

[0015] Please see attached Figure 1 The present application provides a method for optimizing the bonding between a shirt base material and an adhesive strip, the method specifically comprising the following steps:

[0016] Step 1: Perform multi-position base material analysis and clustering on the first shirt base material in the preset bonding operation position to generate multi-position shirt base material properties.

[0017] Specifically, the preset bonding operation position refers to the pre-set operation area for bonding the shirt base material and the adhesive strip when the shirt bonding process is performed. The first shirt base material refers to the shirt material to be bonded with the adhesive strip currently located at the preset bonding operation position, which can be a shirt base material composed of cotton, linen, silk or a combination of different materials. The material types at different positions on the first shirt base material may be different, and the properties of the natural base material are also different. The component materials at multiple positions on the first shirt base material are received, and the properties of the materials at each position are analyzed to determine the properties such as elasticity and softness, and the base material positions with similar properties are classified so that the subsequent bonding process can be carried out more accurately and efficiently. For example, the base materials at two adjacent positions have similar properties, and they are clustered into the same group. In this way, the same adhesive and process parameters can be used during bonding to ensure the quality and effect of bonding. According to the aggregation results, the areas with similar properties are aggregated together as one position, thereby generating the properties of the shirt base material at multiple positions.

[0018] Step 2: calling the adhesive strip formula mapping library, mapping the adhesive strip formulas for the multi-position shirt base material properties, and generating multiple local adhesive strip formulas.

[0019] Specifically, in the shirt manufacturing process, different base material properties may require different types of adhesive strip formulas to achieve the best bonding effect. For example, cotton material may require an adhesive strip formula that can penetrate the fiber, while silk material may require a gentler formula that will not damage the delicate surface. The adhesive strip formula mapping library is a database containing a variety of adhesive strip formulas and their applicable base material properties, which is constructed by professional and technical personnel in this field combined with historical data. The multi-position shirt base material properties are input into the adhesive strip formula mapping library to find the adhesive strip formula that matches the base material properties at each position. For example, if the shirt base material properties of a cluster group are cotton, thinner in thickness and lighter in color, then the mapping library may recommend an adhesive strip formula designed for thin cotton materials with a more transparent color, thereby generating multiple local adhesive strip formulas corresponding to multiple positions.

[0020] Step three: construct multiple local bonding process spaces based on the multiple local adhesive strip recipes, wherein any local bonding process space includes multiple local bonding process composite parameters.

[0021] Specifically, the construction of multiple local bonding process spaces is based on the requirements of different local adhesive strip formulas, and is a set of bonding process parameters customized for each local shirt base material position. Each local bonding process space corresponds to a specific shirt base material position, such as cuffs, collars, etc., and contains multiple local bonding process composite parameters. The local bonding process composite parameters include adhesive properties, adhesive coating amount, drying parameters, curing parameters, bonding pressure and bonding speed, which can be specifically constructed in combination with historical bonding data. By constructing multiple local bonding process spaces, support is provided for subsequent bonding optimization.

[0022] Step 4: calling the bonding adaptation evaluation function, combining the multiple local bonding process spaces to optimize the local bonding process, and generating multiple local optimal bonding process composite parameters.

[0023] Specifically, the bonding adaptation evaluation function is a function for evaluating and optimizing bonding process parameters. This function is capable of evaluating and adjusting the composite parameters in the local bonding process space to find the optimal bonding process parameters for each local position.

[0024] The bonding adaptation evaluation function considers bonding strength, durability, bonding appearance and surface uniformity to generate multiple local optimal bonding process composite parameters for each local location, ensuring that the bonding process at each location is optimized, thereby improving overall production efficiency and quality.

[0025] Step 5: Controlling the bonding of the first shirt base material using the multiple local adhesive strip formulas and the multiple local optimal bonding process composite parameters.

[0026] Specifically, multiple local adhesive strip formulas and multiple local optimal bonding process composite parameters are used as bonding processes, and the appropriate adhesive strip formula and bonding parameters are used for each local position to control the bonding of the first shirt base material, thereby achieving precise control of the bonding operation at different positions on the shirt base material. It is ensured that the bonding at each position on the shirt base material is carried out according to its specific needs and properties, thereby improving the overall quality and durability of the shirt.

[0027] Further, as attached Figure 2 As shown, step three of this application includes:

[0028] A bonding control center connected to the preset bonding operation position is constructed to construct an adhesive property set; a first local adhesive strip formula is extracted from the multiple local adhesive strip formulas, and historical bonding parameter mining is performed in combination with the adhesive property set and the first local bonding area size to generate multiple groups of first local bonding process composite parameters, any group of first local bonding process composite parameters includes adhesive properties, adhesive coating amount, drying parameters, curing parameters, bonding pressure and bonding speed; multi-dimensional parameter cross-variation processing is performed on the multiple groups of first local bonding process composite parameters to obtain multiple groups of first variation composite parameters; a first local bonding process space is constructed with the multiple groups of first local bonding process composite parameters and the multiple groups of first variation composite parameters, and added to the multiple local bonding process spaces.

[0029] Specifically, the bonding control center connected to the preset bonding operation position is to construct an adhesive attribute set, which includes all the adhesive types that can be used for bonding operations, such as the types of adhesives that can be used in the bonding operation workshop, which need to be determined in combination with actual conditions. Any one of the multiple local adhesive strip formulas is extracted and recorded as the first local adhesive strip formula. Combined with the adhesive attribute set and the size of the first local bonding area, historical bonding parameter mining is carried out to find out the bonding process parameters suitable for this position from past production data. These parameters include adhesive properties, adhesive coating amount, drying parameters, curing parameters, bonding pressure and bonding speed, thereby generating multiple sets of first local bonding process composite parameters.

[0030] Then, a multi-dimensional parameter cross-mutation process is performed on multiple sets of first local bonding process composite parameters. Cross-mutation is an optimization algorithm that combines and mutates parameters by simulating the principles of natural selection and genetics to find a better solution. This process can generate new parameter combinations, namely multiple sets of first variation composite parameters, which may have better bonding effects than the original parameter combinations. Then, multiple sets of first local bonding process composite parameters and multiple sets of first variation composite parameters are combined to construct the first local bonding process space. This space contains all possible bonding process parameter combinations and provides rich options for bonding at specific locations on the shirt base. Adding the first local bonding process space to multiple local bonding process spaces can form a comprehensive bonding process parameter library, which provides guidance for the production of the entire shirt and facilitates improving the accuracy and efficiency of the bonding process parameters.

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

[0032] Extract a first local bonding process space according to the multiple local bonding process spaces, and randomly extract a first optimization composite parameter; process the first optimization composite parameter through a bonding effect analyzer to generate a first optimization effect indicator set, wherein the first optimization effect indicator set includes a first bonding strength parameter, a first durability parameter, a first bonding appearance parameter, and a first surface uniformity parameter; obtain a preset bonding requirement parameter set, wherein the preset bonding requirement parameter set includes a preset bonding operation position bonding strength parameter, a preset bonding operation position durability parameter, a preset bonding operation position bonding appearance parameter, and a preset bonding operation position surface uniformity parameter; calculate the The first bonding strength matching index, the first durability matching index, the first bonding appearance evaluation index and the first surface uniformity evaluation index are generated by the parameter ratio of the first optimization effect index set and the preset bonding requirement parameter set, and are substituted into the bonding adaptation evaluation function to generate the first bonding fitness; the second optimization composite parameter is continuously randomly extracted in the first local bonding process space, and the second bonding fitness is calculated and obtained; the first bonding fitness and the second bonding fitness are subjected to fitness discrimination verification, and iterative optimization is performed to generate the first local optimal bonding process composite parameter, and the first local optimal bonding process composite parameter is added into the multiple local optimal bonding process composite parameters.

[0033] Furthermore, the present application also includes the following steps:

[0034] Determine whether the second bonding fitness is greater than the first bonding fitness. If so, obtain the direction of change of the multidimensional process parameters from the first bonding fitness to the second bonding fitness; use the direction of change of the multidimensional process parameters as a pseudo-random constraint, randomly extract a third optimization composite parameter in the first local bonding process space, and calculate and obtain the third bonding fitness; if the third bonding fitness is greater than the second bonding fitness, continue to repeat the analysis according to the pseudo-random constraint, extract the Nth optimization composite parameter and the N+1th optimization composite parameter, and calculate and obtain the Nth bonding fitness and the N+1th bonding fitness; if the N+1th bonding fitness is less than the Nth bonding fitness, perform a fitness change impact analysis of a single parameter based on the Nth optimization composite parameter, and locate multiple optimal single process parameters; use the multiple optimal single process parameters to form the first local optimal bonding process composite parameters.

[0035] Specifically, any local bonding process space is extracted from multiple local bonding process spaces and recorded as the first local bonding process space. A local bonding process composite parameter is randomly extracted from the first local bonding process space and recorded as the first optimization composite parameter, which is used as the starting point in the initial stage of optimization. The first optimization composite parameter is processed by the bonding effect analyzer to generate a first optimization effect index set. The first optimization effect index set includes a first bonding strength parameter, a first durability parameter, a first bonding appearance parameter, and a first surface uniformity parameter, which respectively evaluate different aspects of the bonding effect. Among them, the bonding effect analyzer is an intelligent evaluation model based on an existing machine learning model, such as a neural network model, and then trained with a large amount of sample data. The training of the machine learning model is a common technical means used by those skilled in the art and will not be expanded here.

[0036] The preset bonding requirement parameter set is pre-set by technical personnel in this field according to the shirt design and production requirements, and includes the bonding strength parameters, durability parameters, bonding appearance parameters and surface uniformity parameters of the preset bonding operation position. These parameters are the basic requirements of the manufacturer for the bonding effect of the shirt. The parameter ratio of the same indicator attribute in the first optimization effect index set and the preset bonding requirement parameter set is calculated in order to evaluate the matching degree between the first optimization composite parameter and the preset requirements, generate the first bonding strength matching index, the first durability matching index, the first bonding appearance evaluation index and the first surface uniformity evaluation index, and substitute them into the bonding adaptation evaluation function to generate the first bonding fitness. Among them, the expression of the bonding adaptation evaluation function is: .in, Characterize adhesive fitness; Characterizes the fitness constant factor, Greater than 1; S represents the bonding strength matching index; D represents the durability matching index; A represents the bonding appearance evaluation index; U represents the surface uniformity evaluation index; , , and Characterize the weights of the bonding strength matching index, durability matching index, bonding appearance evaluation index and surface uniformity evaluation index. The expression of the bonding adaptation evaluation function is a mathematical model that comprehensively considers factors such as bonding strength, durability, appearance and surface uniformity, and is used to evaluate the adaptability of bonding process parameters.

[0037] Continue to randomly extract the second optimal composite parameter in the first local bonding process space, and calculate the second bonding fitness. The second optimal composite parameter is different from the first optimal composite parameter. This process is iterative, and each iteration randomly selects a new parameter combination and calculates its fitness.

[0038] The purpose of performing fitness discrimination check on the first bonding fitness and the second bonding fitness is to compare the advantages and disadvantages of different parameter combinations. If the second bonding fitness is higher, it means that the new parameter combination is more in line with the preset bonding requirements, then this new parameter combination will be retained and used for the next round of iteration. This process continues until the first local optimal bonding process composite parameters are found. Finally, the first local optimal bonding process composite parameters will be added to multiple local optimal bonding process composite parameters to provide the optimal process parameters for bonding at a specific location on the shirt base material. Through iterative optimization and fitness evaluation, the optimization of the bonding process parameters is ensured, thereby improving the production quality of the shirt.

[0039] Among them, the specific steps of performing fitness discrimination check on the first bonding fitness and the second bonding fitness, performing iterative optimization, and generating the first local optimal bonding process composite parameters include: judging whether the second bonding fitness is greater than the first bonding fitness is to determine whether the current optimization composite parameters need to be updated. If the second bonding fitness is higher, it means that the new parameter combination is more in line with the preset bonding requirements, so it is necessary to obtain the direction of change of the multidimensional process parameters from the first bonding fitness to the second bonding fitness, and use it as a pseudo-random constraint to guide subsequent parameter selection to maintain the continuity and directionality of the optimization process.

[0040] In the first local bonding process space, the third optimization composite parameter is randomly extracted according to the pseudo-random constraint, and the third bonding fitness is calculated. If the third bonding fitness is greater than the second bonding fitness, it means that the optimization process is still moving in a more optimal direction. The analysis can be repeated according to the pseudo-random constraint to extract the Nth optimization composite parameter and the N+1th optimization composite parameter, and calculate the bonding fitness. If the N+1th bonding fitness is less than the Nth bonding fitness, it means that the current optimization process may have reached a local optimal solution. In this case, the fitness change impact analysis of a single parameter is performed based on the Nth optimization composite parameter, with the aim of locating multiple optimal single process parameters, that is, adjusting a single process parameter, such as a drying parameter, separately, and other parameters must change, analyzing its impact on the bonding fitness, and obtaining the parameter that makes the bonding fitness reach the highest, as the optimal single process parameter, so as to adjust the parameter more accurately to improve the bonding fitness.

[0041] Finally, multiple optimal single process parameters are combined to form the first local optimal bonding process composite parameters. This parameter combination is based on iterative optimization and parameter impact analysis, and represents the optimal process parameters that best meet the preset bonding requirements under current conditions. Through iterative optimization, fitness evaluation and parameter impact analysis, the continuity and directionality of the optimization process are guaranteed, the optimization efficiency is improved, and the optimization of bonding process parameters is ensured, thereby improving the production quality of shirts.

[0042] Furthermore, the present application also includes the following steps:

[0043] The preset bonding operation position is configured with an image monitoring module and a spectral analysis module, wherein the image monitoring module is used to monitor the replacement of shirt base material at the preset bonding operation position, and the spectral analysis module is used to detect the base material composition of the shirt base material, and the image monitoring module is in a normally open state in the bonding operation time zone; when the image monitoring module outputs a shirt base material replacement signal, the spectral analysis module is activated to perform multi-position base material composition detection on the second shirt base material replaced in the preset bonding operation position, and output multi-position base material composition information; based on the multi-position base material composition information, multi-position base material property analysis is performed, the analysis result is compared with the multi-position shirt base material property, it is determined whether a bonding parameter continuous execution signal is generated, and bonding optimization is performed on the replaced shirt base material based on the determination result.

[0044] Furthermore, the present application also includes the following steps:

[0045] Determine whether the analysis result is consistent with the properties of the multi-position shirt base material, and if so, generate the bonding parameter continuous execution signal; call the multiple local adhesive strip formulas and the multiple local optimal bonding process composite parameters according to the bonding parameter continuous execution signal to control the bonding of the replaced second shirt base material.

[0046] Furthermore, the present application also includes the following steps:

[0047] If the analysis result is inconsistent with the properties of the shirt base material at multiple positions, a re-optimization signal is generated; according to the re-optimization signal, adhesive strip formula mapping and local bonding process optimization are performed based on the analysis result.

[0048] Specifically, the image monitoring module and spectral analysis module configured at the preset bonding operation position are devices used to improve the automation and accuracy of the base material replacement and detection during the shirt manufacturing process. The image monitoring module is used to monitor whether the shirt base material at the preset bonding operation position has been replaced. This module is in a normally open state during the bonding operation time zone, which means that it works continuously and monitors the status of the shirt base material in real time. When the image monitoring module detects that the shirt base material has been replaced, it will output a signal, which activates the spectral analysis module.

[0049] The spectrum analysis module is used to perform multi-position base material composition detection on the second shirt base material after replacement. This module can analyze the chemical composition of the base material, such as material, color, etc. Through multi-position detection, the composition of the base material at different positions can be obtained, and multi-position base material composition information can be generated.

[0050] Based on the multi-position base material component information, multi-position base material property analysis is performed, that is, material properties are extracted according to the components. Specifically, professional and technical personnel in this field can construct a material component-property matching library containing the properties corresponding to different material components, so as to match the corresponding properties in the material component-property matching library according to the multi-position base material component information, and generate analysis results containing the base material properties of multiple positions.

[0051] Compare the analysis results with the properties of the multi-position shirt base material. If the analysis results are consistent with the previous base material properties, it means that the replaced base material is similar to the previous base material, and the previous bonding parameters can continue to be used for bonding control. In this case, a bonding parameter continuous execution signal will be generated, indicating that the existing bonding process parameters can continue to be used. If the analysis results do not match the previous base material properties, it means that the replaced base material is quite different from the previous base material, and it is necessary to re-optimize the bonding. In this case, a bonding parameter continuous execution signal will not be generated, indicating that it is necessary to optimize according to the above steps based on the new base material properties. In this way, it can be ensured that each time the base material is changed, the bonding process can be adjusted according to the actual properties of the base material, thereby improving the production quality and efficiency of the shirt.

[0052] Specifically, judging whether the analysis results are consistent with the properties of the multi-position shirt base material is to determine whether the replaced shirt base material has similar properties to the previous base material. If they are consistent, it means that the base material replacement has not changed the bonding requirements of the base material, so a bonding parameter continuous execution signal can be generated. This signal indicates that the existing bonding process parameters can continue to be used for production without the need to re-do the bonding optimization process.

[0053] According to the bonding parameter continuous execution signal, multiple local adhesive strip formulas and multiple local optimal bonding process composite parameters are called to control the bonding of the second shirt base material. In other words, the previously optimized adhesive strip formula and process parameters can be directly used to bond the new shirt base material to ensure the consistency and efficiency of the bonding effect.

[0054] When the analytical results are inconsistent with the properties of the shirt base material at multiple locations, it means that the characteristics of the replaced shirt base material are different from before, and different bonding process parameters may be required to ensure the bonding effect. In this case, a re-optimization signal is generated, indicating that the bonding process needs to be re-optimized. According to the re-optimization signal, the adhesive strip formula mapping and local bonding process optimization are performed based on the analytical results, that is, the previous optimization steps, including adhesive strip formula mapping and local bonding process optimization, are repeated. New local optimal bonding process composite parameters are generated, which are optimized based on the characteristics of the replaced shirt base material. Using these new parameters for bonding control can ensure that under the new base material conditions, the bonding effect of the shirt still meets the preset standards, thereby ensuring the consistency and reliability of product quality. Re-optimization and process optimization based on analytical results ensure that the shirt manufacturing process can flexibly respond to changes brought about by base material replacement and improve the adaptability and efficiency of the production process.

[0055] In summary, the method for optimizing the bonding between a shirt base material and an adhesive strip provided in this application has the following technical effects:

[0056] 1. Perform multi-position base material analysis and clustering on the first shirt base material in the preset bonding operation position to generate multi-position shirt base material properties; call the adhesive strip formula mapping library to perform adhesive strip formula mapping on the multi-position shirt base material properties to generate multiple local adhesive strip formulas; construct multiple local bonding process spaces based on the multiple local adhesive strip formulas, wherein any local bonding process space contains multiple local bonding process composite parameters; call the bonding adaptation evaluation function, combine the multiple local bonding process spaces to optimize the local bonding process, and generate multiple local optimal bonding process composite parameters; use the multiple local adhesive strip formulas and the multiple local optimal bonding process composite parameters to control the bonding of the first shirt base material. Through multi-position base material analysis and clustering, multiple local adhesive strip formulas can be generated for different shirt base materials, and multiple local bonding process spaces can be constructed to optimize the bonding process at local positions, thereby achieving local precise control of the bonding between the shirt base material and the adhesive strip, thereby achieving the technical effect of ensuring the bonding quality at local positions and improving the quality of shirt production.

[0057] 2. An image monitoring module and a spectrum analysis module are configured at a preset bonding operation position. When the image monitoring module outputs a shirt base material replacement signal, the spectrum analysis module is activated to perform multi-position base material component detection on the second shirt base material replaced in the preset bonding operation position, and output multi-position base material component information; multi-position base material property analysis is performed based on the multi-position base material component information, and the analysis result is compared with the multi-position shirt base material property to determine whether to generate a bonding parameter continuous execution signal. Based on the determination result, bonding optimization is performed on the replaced shirt base material, thereby ensuring that the shirt manufacturing process can flexibly respond to changes brought about by base material replacement, achieving the technical effect of ensuring the continuity and stability of the bonding process and improving the adaptability and efficiency of the production process.

[0058] 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.

[0059] 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 optimizing the bonding between a shirt base material and an adhesive strip, characterized in that: include: Performing multi-position base material analysis and clustering on the first shirt base material in the preset bonding operation position to generate multi-position shirt base material properties; Calling a glue strip formula mapping library, mapping glue strip formulas for the properties of the shirt base material at multiple positions, and generating multiple local glue strip formulas; constructing a plurality of local bonding process spaces based on the plurality of local adhesive strip recipes, wherein any local bonding process space comprises a plurality of local bonding process composite parameters; Calling a bonding adaptation evaluation function, combining the multiple local bonding process spaces to perform local bonding process optimization, and generating multiple local optimal bonding process composite parameters; Controlling the bonding of the first shirt base material using the multiple local adhesive strip formulas and the multiple local optimal bonding process composite parameters; Wherein, multiple local bonding process spaces are constructed based on the multiple local adhesive strip recipes, including: Connecting the bonding control center of the preset bonding operation position to construct a set of adhesive properties; Extracting a first local adhesive strip formula from the multiple local adhesive strip formulas, and performing historical adhesive parameter mining in combination with the adhesive property set and the first local adhesive area size to generate multiple groups of first local adhesive process composite parameters, wherein any group of first local adhesive process composite parameters includes adhesive properties, adhesive coating amount, drying parameters, curing parameters, adhesive pressure, and adhesive speed; Performing multi-dimensional parameter cross-variation processing on the multiple groups of first local bonding process composite parameters to obtain multiple groups of first variation composite parameters; Constructing a first local bonding process space with the multiple sets of first local bonding process composite parameters and the multiple sets of first variation composite parameters, and adding the first local bonding process space into the multiple local bonding process spaces; The bonding adaptation evaluation function is called to optimize the local bonding process in combination with the multiple local bonding process spaces to generate multiple local optimal bonding process composite parameters, including: Extracting a first local bonding process space according to the plurality of local bonding process spaces, and randomly extracting a first optimization composite parameter; Processing the first optimization composite parameter by an adhesion effect analyzer to generate a first optimization effect index set, wherein the first optimization effect index set includes a first adhesion strength parameter, a first durability parameter, a first adhesion appearance parameter, and a first surface uniformity parameter; Acquire a preset bonding requirement parameter set, wherein the preset bonding requirement parameter set includes a preset bonding operation position bonding strength parameter, a preset bonding operation position durability parameter, a preset bonding operation position bonding appearance parameter, and a preset bonding operation position surface uniformity parameter; Calculating the parameter ratio of the same indicator attribute in the first optimization effect index set and the preset bonding requirement parameter set, generating a first bonding strength matching index, a first durability matching index, a first bonding appearance evaluation index and a first surface uniformity evaluation index, and substituting them into the bonding adaptation evaluation function to generate a first bonding fitness; Continue to randomly extract a second optimal composite parameter in the first local bonding process space, and calculate and obtain a second bonding fitness; Performing fitness discrimination verification on the first bonding fitness and the second bonding fitness, performing iterative optimization, generating a first local optimal bonding process composite parameter, and adding the first local optimal bonding process composite parameters to the multiple local optimal bonding process composite parameters; The expression of the bonding adaptation evaluation function is: ； in, Characterize adhesive fitness; Characterizes the fitness constant factor, Greater than 1; S represents the bonding strength matching index; D represents the durability matching index; A represents the bonding appearance evaluation index; U represents the surface uniformity evaluation index; , , and Characterizing the weights of the bonding strength matching index, the durability matching index, the bonding appearance evaluation index, and the surface uniformity evaluation index; The first bonding fitness and the second bonding fitness are subjected to fitness discrimination verification, and iterative optimization is performed to generate first local optimal bonding process composite parameters, including: Determine whether the second bonding fitness is greater than the first bonding fitness, and if so, obtain a change direction of the multi-dimensional process parameters from the first bonding fitness to the second bonding fitness; Taking the change direction of the multi-dimensional process parameters as a pseudo-random constraint, randomly extracting a third optimization composite parameter in the first local bonding process space, and calculating and obtaining a third bonding fitness; If the third bonding fitness is greater than the second bonding fitness, continue to repeat the analysis according to the pseudo-random constraint, extract the Nth optimization composite parameter and the N+1th optimization composite parameter, and calculate and obtain the Nth bonding fitness and the N+1th bonding fitness; If the N+1th bonding fitness is less than the Nth bonding fitness, performing fitness change impact analysis of a single parameter based on the Nth optimization composite parameter to locate multiple optimal single process parameters; The first local optimal bonding process composite parameter is composed of the multiple optimal single process parameters.

2. The method for optimizing the bonding between a shirt base material and an adhesive strip according to claim 1, characterized in that: Also includes: The preset bonding operation position is configured with an image monitoring module and a spectrum analysis module, wherein the image monitoring module is used to monitor the replacement of the shirt base material at the preset bonding operation position, and the spectrum analysis module is used to detect the base material composition of the shirt base material, and the image monitoring module is in a normally open state in the bonding operation time zone; When the image monitoring module outputs a shirt base material replacement signal, the spectrum analysis module is activated to perform multi-position base material component detection on the second shirt base material replaced in the preset bonding operation position, and output multi-position base material component information; The multi-position base material properties are analyzed based on the multi-position base material component information, the analysis result is compared with the multi-position shirt base material properties, it is determined whether to generate a bonding parameter continuous execution signal, and the bonding optimization of the replaced shirt base material is performed based on the determination result.

3. The method for optimizing the bonding between a shirt base material and an adhesive strip according to claim 2, characterized in that: The method further comprises: performing a multi-position bottom material property analysis based on the multi-position bottom material component information, comparing the analysis result with the multi-position bottom material properties of the shirt, determining whether to generate a bonding parameter continuous execution signal, and performing bonding optimization on the replaced bottom material of the shirt based on the determination result, including: Determine whether the analysis result is consistent with the properties of the shirt base material at multiple positions, and if so, generate a bonding parameter continuous execution signal; The plurality of local adhesive strip recipes and the plurality of local optimal bonding process composite parameters are called according to the bonding parameter continuous execution signal to perform bonding control on the replaced second shirt base material.

4. The method for optimizing the bonding between a shirt base material and an adhesive strip according to claim 3, characterized in that: include: If the analysis result is inconsistent with the properties of the shirt base material at multiple positions, a re-optimization signal is generated; According to the re-optimization signal, adhesive strip recipe mapping and local bonding process optimization are performed based on the analytical results.

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