Manufacturing control method and system for metal screen cloth

Abstract

The invention provides a metal screen cloth manufacturing control method and system, and relates to the technical field of metal screen cloth processing, and the method comprises the steps: obtaining application scene information of target metal screen cloth, carrying out the multi-dimensional demand factor analysis, and obtaining metal screen cloth manufacturing demand factor parameters; coding is carried out, metal screen cloth manufacturing factor coding information is generated, matching screening is carried out from a metal screen cloth process chain, and a metal screen cloth manufacturing process node set is obtained; manufacturing data mining is carried out, and a manufacturing process node space set is constructed; performing optimization analysis, and determining a node manufacturing control parameter set; and quality feedback optimization is carried out, a node manufacturing optimization control parameter set is obtained, and adaptive manufacturing control is carried out. The technical problems that in the prior art, in the metal screen cloth manufacturing process, fixed technological parameters are often adopted, self-adaptive adjustment and optimization capacity is lacked, and consequently the production efficiency and the product quality are poor are solved.

Description

Technical Field

[0001] The invention relates to the technical field of metal mesh cloth processing, and in particular to a manufacturing control method and system of a metal mesh cloth. Background Art

[0002] Metal mesh is an important material widely used in many fields. When manufacturing metal mesh, the existing technology usually lacks comprehensive analysis of specific application scenarios and only produces according to general standards, which cannot fully meet the special needs of various application scenarios, resulting in poor performance of the manufactured metal mesh in some special application scenarios. In addition, the traditional metal mesh manufacturing process usually adopts fixed process parameters and lacks the ability to make intelligent adjustments and optimizations according to the actual situation in the production process. In actual production, different batches of raw materials and process conditions may vary, and fixed process parameters cannot cope with these changes, resulting in large fluctuations in product quality and low production efficiency. These problems seriously restrict the production efficiency and product quality of metal mesh. Summary of the invention

[0003] The present application provides a metal mesh manufacturing control method and system, aiming to solve the technical problem that the prior art often uses fixed process parameters in the metal mesh manufacturing process, lacks adaptive adjustment and optimization capabilities, and leads to poor production efficiency and product quality.

[0004] The first aspect disclosed in the present application provides a manufacturing control method for a metal mesh cloth, the method comprising: obtaining application scenario information of a target metal mesh cloth, performing a multi-dimensional demand factor analysis on the application scenario information, and obtaining metal mesh cloth manufacturing demand factor parameters; encoding the metal mesh cloth manufacturing demand factor parameters to generate metal mesh cloth manufacturing factor encoding information, matching and screening from a metal mesh cloth process chain based on the metal mesh cloth manufacturing factor encoding information to obtain a metal mesh cloth manufacturing process node set; performing manufacturing data mining based on the metal mesh cloth manufacturing process node set to construct a manufacturing process node space set, the manufacturing process node space set including historical node manufacturing control parameters of each metal mesh cloth manufacturing process node and corresponding node manufacturing effect data; performing optimization analysis on the metal mesh cloth manufacturing demand factor parameters based on the manufacturing process node space set to determine a node manufacturing control parameter set; performing quality feedback tuning on the node manufacturing control parameter set to obtain a node manufacturing optimization control parameter set, and performing adaptive manufacturing control on the target metal mesh cloth based on the node manufacturing optimization control parameter set.

[0005] The second aspect disclosed in the present application provides a manufacturing control system for a metal mesh cloth, the system being used for the manufacturing control method for the metal mesh cloth mentioned above, the system comprising: a multidimensional demand factor analysis module, the multidimensional demand factor analysis module being used to obtain application scenario information of a target metal mesh cloth, performing multidimensional demand factor analysis on the application scenario information, and obtaining metal mesh cloth manufacturing demand factor parameters; a matching and screening module, the matching and screening module being used to encode the metal mesh cloth manufacturing demand factor parameters, generate metal mesh cloth manufacturing factor encoding information, perform matching and screening from a metal mesh cloth process chain based on the metal mesh cloth manufacturing factor encoding information, and obtain a metal mesh cloth manufacturing process node set; a manufacturing data mining module, the manufacturing data mining module being used to Manufacturing data mining is performed based on the metal mesh manufacturing process node set to construct a manufacturing process node space set, wherein the manufacturing process node space set includes historical node manufacturing control parameters of each metal mesh manufacturing process node and corresponding node manufacturing effect data; an optimization analysis module is used to perform optimization analysis on the metal mesh manufacturing demand factor parameters based on the manufacturing process node space set to determine a node manufacturing control parameter set; an adaptive manufacturing control module is used to perform quality feedback tuning on the node manufacturing control parameter set, obtain a node manufacturing optimization control parameter set, and perform adaptive manufacturing control on the target metal mesh based on the node manufacturing optimization control parameter set.

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

[0007] By obtaining the application scenario information of the target metal mesh and conducting a multi-dimensional demand factor analysis, specific manufacturing demand factor parameters are obtained to ensure that the manufactured metal mesh can fully meet the specific needs of different application scenarios and improve the applicability and performance of the product; manufacturing data mining is performed based on the metal mesh manufacturing process node set, and a manufacturing process node space set is constructed, which includes the historical manufacturing control parameters and effect data of each node, provides data support for manufacturing process optimization, and improves the reliability of the manufacturing process; based on the manufacturing process node space set, the metal mesh manufacturing demand factor parameters are optimized and analyzed to determine the node manufacturing control parameter set, achieve the global optimum of the manufacturing parameters, and improve the optimization effect of the manufacturing process and product quality; by performing quality feedback tuning on the node manufacturing control parameter set, the node manufacturing optimization control parameter set is obtained, and adaptive manufacturing control is performed based on this, which realizes the intelligent and flexible adjustment of the manufacturing process, and can adaptively adjust the process parameters according to the actual production situation to improve the production efficiency and the stability of product quality. Through the above method, the problems existing in the existing technology in the metal mesh manufacturing process, such as insufficient application scenario analysis, unintelligent manufacturing process, insufficient data utilization, and unsystematic optimization process, are comprehensively solved, the intelligence and flexibility of the manufacturing process are improved, and the overall improvement of product quality and production efficiency is achieved.

[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 in accordance with the contents of the specification. 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 listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic diagram of a manufacturing control method for a metal mesh cloth provided in an embodiment of the present application;

[0010] Figure 2 A schematic diagram of the structure of a metal mesh manufacturing control system provided in an embodiment of the present application.

[0011] Explanation of reference numerals: multi-dimensional demand factor analysis module 10 , matching and screening module 20 , manufacturing data mining module 30 , optimization analysis module 40 , adaptive manufacturing control module 50 . DETAILED DESCRIPTION

[0012] The embodiment of the present application provides a manufacturing control method for a metal mesh cloth, thereby solving the technical problem that in the prior art, fixed process parameters are often used in the manufacturing process of the metal mesh cloth, and the adaptive adjustment and optimization capabilities are lacking, resulting in poor production efficiency and product quality.

[0013] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] like Figure 1 As shown, an embodiment of the present application provides a manufacturing control method for a metal mesh cloth, the method comprising:

[0015] Obtain application scenario information of the target metal mesh cloth, perform multi-dimensional demand factor analysis on the application scenario information, and obtain metal mesh cloth manufacturing demand factor parameters.

[0016] Through customer demand documents, industry standards and other sources, the application scenario information of the target metal mesh is collected, including but not limited to the use environment, use conditions, expected functional characteristics, expected lifespan and user needs. The collected application scenario information is classified into categories such as physical requirements, mechanical requirements, chemical requirements, economic requirements and environmental requirements. For example, physical requirements include size, shape, weight, etc., and mechanical requirements include strength, hardness, wear resistance, etc.

[0017] The demand factors are prioritized through the hierarchical analysis method to determine the importance weight of each factor. Based on the analysis results, specific manufacturing demand factor parameters are extracted. These parameters are concretized into technical indicators in the production process, such as aperture size, mesh shape, material strength, surface treatment requirements, etc.

[0018] The metal mesh cloth manufacturing demand factor parameters are encoded to generate metal mesh cloth manufacturing factor encoding information, and matching and screening are performed from the metal mesh cloth process chain based on the metal mesh cloth manufacturing factor encoding information to obtain a metal mesh cloth manufacturing process node set.

[0019] Obtain the demand factor information of metal mesh, including specifications and dimensions, mechanical properties, mesh surface flatness, mesh shape and aperture, and physical and chemical properties, etc. Use the metal mesh knowledge base to classify and fill in the demand factor information to form a complete set of demand factor content.

[0020] The content set of metal mesh demand factors is used as a coding element, that is, the content that needs to be coded. Each demand factor content is used as a coding element. At the same time, the coding order is defined, such as encoding the specification size first, then encoding the mechanical properties, etc., determining the length of each coding element, and defining the coding symbols used, such as numbers, letters, etc. Based on the above elements, order, length and symbols, formulate specific coding rules. Using the formulated coding rules, encode the parameters of metal mesh manufacturing demand factors to generate metal mesh manufacturing factor coding information.

[0021] According to the metal mesh process chain, a relevant database is obtained, which contains the process record data of each manufacturing process node, such as the process parameters, process conditions, operating procedures, etc. of each node. The process record data information of each manufacturing process node is labeled to form node manufacturing demand label characteristic information. Using the above encoding rules, the node manufacturing demand label characteristic information is encoded to obtain the process chain node manufacturing demand encoding set.

[0022] The metal mesh manufacturing factor coding information and the process chain node manufacturing demand coding set are matched and screened according to the consistency or similarity of the coding information, and the process nodes that meet the coding information are screened out to form a metal mesh manufacturing process node set.

[0023] Through the above steps, the metal mesh manufacturing demand factors can be systematically encoded, and the manufacturing process nodes that meet the requirements can be screened out from the process chain based on the coding information to form a complete set of process nodes.

[0024] Furthermore, the generation of the metal mesh manufacturing factor coding information includes:

[0025] Obtaining metal mesh demand factor information, the metal mesh demand factor information including specification size, mechanical properties, mesh surface flatness, mesh shape and aperture, and physical and chemical properties; classifying and filling the metal mesh demand factor information through a metal mesh knowledge base to obtain a metal mesh demand factor content set; using the metal mesh demand factor content set as a coding element, designing a coding sequence, coding length, and coding symbols; determining a metal mesh manufacturing coding rule based on the coding element and the coding sequence, coding length, and coding symbols; encoding the metal mesh manufacturing demand factor parameters using the metal mesh manufacturing coding rule to generate the metal mesh manufacturing factor coding information.

[0026] Obtain information on metal mesh demand factors, where specifications include length, width, thickness, etc.; mechanical properties include tensile strength, yield strength, elongation, etc.; mesh surface flatness refers to the flatness of the mesh surface, which is usually divided into several levels; mesh shape and aperture refer to the geometric shape and aperture size of the mesh, such as square, hexagonal, diamond, etc.; physical and chemical properties include corrosion resistance, conductivity, thermal stability, etc.

[0027] The Metal Mesh Knowledge Base is a pre-built database that contains various technical standards, material performance data, manufacturing process information and application cases of metal mesh, such as application examples and effects of metal mesh in different scenarios.

[0028] According to the application requirements and technical standards of metal mesh, the demand factor information is classified, for example, according to the specification size, according to length, width, thickness, etc.; according to the mechanical properties, according to tensile strength, yield strength, elongation, etc. The classified demand factor information is integrated with the data in the knowledge base, that is, the corresponding technical parameters are matched from the knowledge base, the demand factor information is improved, and a complete set of demand factor content is formed.

[0029] List all the metal mesh demand factors, that is, the content that needs to be coded, and each demand factor content is used as a coding element. Design a specific coding order, coding length and coding symbol for each coding element. The coding order determines the arrangement position of each element in the coding string; the coding length determines the number of characters each element occupies in the coding string; the coding symbol determines which characters, such as numbers and letters, are used to represent each element.

[0030] According to the determined coding elements, coding order, coding length and coding symbols, specific coding rules are combined.

[0031] According to the established coding rules, the specific metal mesh manufacturing demand factor parameters are encoded. Specifically, according to the coding rules, according to the coding elements, coding length and coding symbols, the actual value of each demand factor parameter is converted into a corresponding coding representation, and all coding elements are combined into a complete coding string in the prescribed coding order to generate standardized metal mesh manufacturing factor coding information.

[0032] Furthermore, the step of obtaining a set of metal mesh manufacturing process nodes includes:

[0033] A metal mesh process database is obtained according to the metal mesh process chain, wherein the metal mesh process database includes process record data information of each manufacturing process node in the metal mesh process chain; the process record data information of each manufacturing process node is labeled with manufacturing requirements to obtain node manufacturing requirement label characteristic information; the node manufacturing requirement label characteristic information is encoded according to the metal mesh manufacturing coding rule to obtain a process chain node manufacturing requirement coding set; matching and screening are performed based on the metal mesh manufacturing factor coding information and the process chain node manufacturing requirement coding set to obtain a metal mesh manufacturing process node set.

[0034] The manufacturing process chain of metal mesh cloth is all the manufacturing steps and process flows from raw materials to final products. Each process node represents a specific manufacturing step, such as stretching, cutting, welding, heat treatment, surface treatment, etc. Collect historical process record data for each process node, including equipment parameters, operating conditions, process time, environmental conditions, etc. These data can come from production logs, sensor data, quality inspection reports, etc. Design a database structure to store the record data of all process nodes. The data of each process node should be stored independently and associated with the data of other nodes to obtain the metal mesh cloth process database.

[0035] According to the manufacturing requirements of metal mesh, a standardized labeling system is defined. The labels can include parameter ranges (such as temperature range), quality standards (such as surface roughness level), operating conditions (such as pressure range), etc. The process record data of each process node is converted into a standardized label to indicate the manufacturing requirements that can be met by the node. Labeling helps to quickly match manufacturing requirements with process capabilities. By labeling the process record data of each process node, the node manufacturing requirement label characteristic information is generated.

[0036] According to the previously formulated metal mesh manufacturing coding rules, the process node manufacturing requirement label characteristic information is encoded. The coding rules include coding order, coding length, coding symbols, etc. According to the coding rules, each label is converted into a corresponding coding representation and combined into a complete node manufacturing requirement coding set.

[0037] Extract the metal mesh manufacturing factor coding information and process chain node manufacturing requirement coding set from the database, compare the manufacturing factor coding information with the process node manufacturing requirement coding one by one, calculate the matching degree, and screen out the process nodes that meet the manufacturing requirements based on the matching results. Based on the matching screening results, form a metal mesh manufacturing process node set, which includes all process nodes that meet specific manufacturing requirements. This process ensures the precise control and optimization of the manufacturing process, which helps to improve production efficiency and product quality.

[0038] Manufacturing data mining is performed based on the metal mesh manufacturing process node set to construct a manufacturing process node space set, which includes historical node manufacturing control parameters of each metal mesh manufacturing process node and corresponding node manufacturing effect data.

[0039] Based on the manufacturing process node set, the historical manufacturing data of each corresponding node is collected, including manufacturing control parameters and manufacturing effect data. The collected manufacturing data is preprocessed, including cleaning abnormal data and noise data, to ensure the accuracy and completeness of the data.

[0040] The manufacturing process node space set is constructed based on the preprocessed data. The node space set is a space set constructed for each manufacturing process node. The set contains all the historical manufacturing data of the node. Each node space set contains two main parts. The historical node manufacturing control parameters are the historical manufacturing control parameters of each manufacturing node. For example, for a certain heat treatment node, the control parameters may include temperature, heating time, cooling rate, etc.; the node manufacturing effect data is the manufacturing effect data of each manufacturing node. For example, for the same heat treatment node, the effect data may include product hardness, surface quality, dimensional accuracy, etc.

[0041] Based on the manufacturing process node space set, the metal mesh manufacturing demand factor parameters are respectively optimized and analyzed to determine the node manufacturing control parameter set.

[0042] Obtain node parameter optimization factor information, including optimization target complexity, node data characteristics, and allocated computing resources. Based on the node parameter optimization factor information, perform multi-factor analysis on the manufacturing process node space set to analyze the influence of each manufacturing control parameter on the manufacturing effect. For example, use correlation analysis, variance analysis and other methods to determine the influence of parameters such as temperature, time, cooling rate, etc. on product hardness.

[0043] Based on the results of multi-factor analysis, select an adaptive optimization algorithm. For example, for nonlinear and multi-peak optimization problems, you can choose a genetic algorithm; for continuous optimization problems, you can choose a particle swarm optimization algorithm to obtain a node adaptation parameter optimization algorithm set. Map the selected optimization algorithm to the manufacturing process node space set, configure related parameters, and initialize the optimization algorithm's optimization parameters, such as the initial population, initial solution, etc.

[0044] The parameters of metal mesh manufacturing demand factors are used as optimization constraints to ensure that the optimization results meet actual needs. Based on the node adaptation parameter optimization algorithm set and optimization parameters, global optimization is performed within the manufacturing process node space set. The manufacturing control parameters are gradually adjusted, and the manufacturing effects of each iteration are calculated and compared. The best parameter combination is selected, and the optimal control parameters of each manufacturing process node are determined through the global optimization process. The optimized manufacturing control parameters form a node manufacturing control parameter set.

[0045] Furthermore, the determining of the node manufacturing control parameter set includes:

[0046] Obtain node parameter optimization factor information, wherein the node parameter optimization factor information includes optimization target complexity, node data characteristics, and allocated computing resources; perform multi-factor analysis on the manufacturing process node space set based on the node parameter optimization factor information to obtain a node adaptation parameter optimization algorithm set; map and configure the node adaptation parameter optimization algorithm set to the manufacturing process node space set, and initialize the optimization algorithm optimization parameters; use the metal mesh manufacturing demand factor parameters as optimization constraints, perform global optimization within the manufacturing process node space set based on the node adaptation parameter optimization algorithm set and the optimization algorithm optimization parameters, and determine the node manufacturing control parameter set.

[0047] Obtain information on node parameter optimization factors. Specifically, clarify the specific goals of node parameter optimization, such as improving product quality, reducing production costs, shortening production time, etc., and evaluate the complexity of the optimization goals, including the diversity of objective functions, the number and types of constraints, conflicts and mutual influences between goals, etc.

[0048] Clarify the characteristics of node data, including data volume, that is, the size of the analysis node data; large data volume, that is, the data volume is huge, suitable for data-driven optimization algorithms; small data volume, that is, the data volume is small, suitable for simulation or model-based optimization algorithms; data type, that is, the type and nature of the data; as well as continuous data, discrete data, data characteristics, noise data, etc.

[0049] Evaluate the computing resources available for the optimization process, including computing power, memory, storage space, etc., and determine whether high computing resources, that is, having strong computing power, are suitable for using computationally complex optimization algorithms; low computing resources, that is, limited computing resources, are suitable for using computationally efficient optimization algorithms.

[0050] Based on the node parameter optimization factor information, the spatial set of manufacturing process nodes is analyzed by multiple factors. When the data volume is large, the node data is analyzed by using data-driven analysis methods, such as machine learning models, such as neural networks, decision trees, random forests, etc. When the data volume is small, the node data is analyzed by using simulation or model-based analysis methods, such as genetic algorithms, simulated annealing, Bayesian optimization, etc. When there is noise or uncertainty in the data, robust analysis methods are used, such as random optimization algorithms, evolutionary algorithms, etc. According to the results of multi-factor analysis, a set of adaptive optimization algorithms is determined for different data characteristics and optimization goals.

[0051] Each manufacturing process node is analyzed to determine its data characteristics, optimization target complexity, and available computing resources. According to the characteristics of each node, the most suitable optimization algorithm is configured from the set of adaptive parameter optimization algorithms. The initial parameters of the optimization algorithm configured for each node are set, including population size, number of iterations, learning rate, etc. Among them, the population size is applicable to evolutionary algorithms such as genetic algorithms, indicating the number of solutions retained in each iteration; the number of iterations is a common parameter for all optimization algorithms, indicating the maximum number of times the algorithm runs; the learning rate is applicable to machine learning algorithms such as gradient descent, indicating the step size of each parameter update.

[0052] The parameters of metal mesh manufacturing demand factors are used as constraints. These constraints ensure that the solutions in the optimization process meet the actual manufacturing needs. According to the set initial parameters, an initial solution or initial population is generated for each node. For evolutionary algorithms, the initial population is generated randomly. For machine learning algorithms, the initial solution is generated using historical data. The optimization algorithm is run in each manufacturing process node space set. The parameters are continuously adjusted according to the optimization constraints, the objective function is optimized, the optimization process of each iteration is monitored, and the solution of each iteration and its corresponding objective function value are recorded. During the optimization iteration process, the optimal solution of each iteration is recorded and analyzed. According to the optimization objectives and constraints, the global optimal solution is selected as the final node manufacturing control parameter set.

[0053] Furthermore, the determining the node manufacturing control parameter set includes:

[0054] The parameters of the metal mesh manufacturing demand factor are used as optimization constraints, and are optimally divided with the manufacturing process node space set to obtain a manufacturing process node memory library set; effect indicators are extracted from the node manufacturing effect data to determine a node manufacturing effect evaluation indicator set, and the node manufacturing effect evaluation indicator set is fitted based on the manufacturing process node space set to construct a node manufacturing effect evaluation fitness function set; based on the node adaptation parameter optimization algorithm set and the optimization algorithm optimization parameters, iterative optimization is performed within the manufacturing process node memory library set, and parameter optimization comparison is performed through the node manufacturing effect evaluation fitness function set to determine the node manufacturing control parameter set.

[0055] The metal mesh manufacturing demand factor parameters are used as optimization constraints for constraint processing in the optimization process. The process node space set is a set containing all manufacturing process nodes and their parameters and effect data. According to the optimization constraints, the manufacturing process node space set is divided to screen out the node sets that meet these conditions. For example, each node is checked to see if its parameters meet the constraints, nodes that meet the conditions are retained, and nodes that do not meet the conditions are eliminated. After screening, a memory library set of all manufacturing process nodes that meet the optimization constraints is obtained.

[0056] Statistical analysis, data mining and other methods are used to extract key effect indicators from the effect data. These indicators can comprehensively reflect the manufacturing effect of the node. For example, these indicators are extracted from data such as hardness, tensile strength, and surface quality as effect indicators to determine a set of effect evaluation indicators.

[0057] Use regression analysis, machine learning models and other methods to fit the effect evaluation indicators to the manufacturing parameters. For example, build a relationship model, use the data in the process node space set to train the fitting model, and determine the influence of each parameter on the effect indicator. According to the fitting results, build a fitness function set to describe the influence of manufacturing parameters on the effect indicators and evaluate the adaptability of manufacturing process nodes. It can be a linear function, nonlinear function, polynomial, etc., determined according to the fitting results.

[0058] According to the configured adaptation algorithm, such as genetic algorithm, simulated annealing, neural network, etc., the optimization algorithm for each process node is determined, and combined with the initial parameters of each algorithm, such as population size, number of iterations, learning rate, etc., iterative optimization is performed in the memory library set of the manufacturing process node. Specifically, for evolutionary algorithms, such as genetic algorithms, an initial population is generated, and for gradient descent algorithms, an initial solution or initial parameters are selected, individuals with high fitness are selected for replication, the selected individuals are cross-fertilized to generate new individuals, the new individuals are randomly mutated to increase the diversity of solutions, and the fitness of each individual is evaluated using a fitness function. According to the evaluation results, the population or solution is updated. For each parameter combination generated by each iteration, its fitness value is calculated. In each iteration, the parameter combination with the highest fitness value is selected, the optimal individual or solution is retained, and the individual or solution with lower fitness is replaced until the iteration termination condition is reached, such as reaching the maximum number of iterations, or the fitness value reaches a preset threshold, or there is no significant improvement after several consecutive iterations.

[0059] When the iteration termination condition is reached, the optimal manufacturing control parameters for each process node are determined based on the final result of the iterative optimization. These parameters meet the manufacturing requirements and perform best in the fitness evaluation. Through the above steps, the iterative optimization is systematically carried out to finally determine the node manufacturing control parameter set, ensuring the efficiency and stability of the manufacturing process, which helps to improve product quality and production efficiency.

[0060] Quality feedback tuning is performed on the node manufacturing control parameter set to obtain a node manufacturing optimization control parameter set, and adaptive manufacturing control is performed on the target metal mesh based on the node manufacturing optimization control parameter set.

[0061] Conduct quality inspection on the manufactured metal mesh and obtain the quality inspection data stream. Analyze the quality inspection data stream and extract the performance parameters to be optimized, including the quality indicators that need to be improved, such as insufficient strength, dimensional deviation, surface defects, etc., to form a set of node quality performance parameters to be optimized.

[0062] Based on the set of quality performance parameters of the nodes to be optimized, the node manufacturing control parameter set is fine-tuned and optimized. For example, sensitivity analysis, multi-objective optimization and other methods are used for fine-tuning. By adjusting parameters such as heat treatment temperature, time, cooling rate, etc., the influence of fine-tuning parameters on product strength is obtained, and a set of optional node manufacturing control parameter thresholds is obtained.

[0063] The node adaptation parameter optimization algorithm set is used to optimize the optional node manufacturing control parameter threshold set respectively, and the node manufacturing optimization control parameter set is obtained, which includes the specific value of the optimized manufacturing control parameter set. Based on the node manufacturing optimization control parameter set, the manufacturing parameters are adjusted through the adaptive control system to realize the adaptive manufacturing control of the target metal mesh.

[0064] Through the above steps, quality feedback tuning is systematically performed to obtain a set of node manufacturing optimization control parameters, and adaptive manufacturing control of the target metal mesh is achieved, thereby continuously improving product quality and production efficiency.

[0065] Furthermore, the obtaining of the node manufacturing optimization control parameter set includes:

[0066] The target metal mesh is subjected to quality inspection feedback through the node manufacturing control parameter set to obtain a metal mesh quality inspection data stream; the performance to be optimized is extracted from the metal mesh quality inspection data stream to determine a node quality performance parameter set to be optimized; based on the node quality performance parameter set to be optimized, the node manufacturing control parameter set is fine-tuned and optimized and analyzed to obtain an optional node manufacturing control parameter threshold set; the node adaptation parameter optimization algorithm set is used to respectively optimize the optional node manufacturing control parameter threshold sets to obtain the node manufacturing optimization control parameter set.

[0067] In the actual production process, the previously determined set of node manufacturing control parameters is applied to manufacture the metal mesh, and the manufactured metal mesh is subjected to comprehensive quality inspection. The inspection items include but are not limited to specifications and dimensions, mechanical properties, mesh flatness, mesh shape and aperture, physical and chemical properties, etc. All quality inspection results are collected through various inspection instruments, including tensile testing machines, microscopes, corrosion testing equipment, etc., to form a metal mesh quality inspection data stream.

[0068] Perform data analysis on the collected quality inspection data stream to identify which performance indicators do not meet the expected requirements or have room for improvement. For example, use statistical analysis, chart display and other methods to conduct a comprehensive analysis of the inspection data to determine specific quality issues such as dimensional deviation, insufficient strength, surface defects, etc.

[0069] The performance indicators that need to be optimized are extracted from the quality inspection data stream. These indicators represent the problems existing in the manufacturing process of the metal mesh. For example, if the inspection data shows that the tensile strength is lower than expected, then the tensile strength is the performance to be optimized. Based on the extracted performance indicators to be optimized, the specific set of node quality performance parameters to be optimized is determined.

[0070] Using the determined set of quality performance parameters of the nodes to be optimized as the basic data for optimization analysis, fine-tuning optimization analysis is performed. For example, sensitivity analysis methods are used to analyze the sensitivity of each control parameter to quality performance, determine which parameters' fine-tuning has the greatest impact on performance, and use regression analysis and other methods to quantify the impact of each control parameter on the target performance index. For example, through experiments, it is determined that temperature changes have the most significant impact on tensile strength. Based on the results of the sensitivity analysis, the adjustable range of each parameter to be optimized is set, that is, the upper and lower limits of each parameter, as well as the adjustable step size, are determined. Through fine-tuning optimization analysis, the optional threshold range of each parameter to be optimized is determined to form a threshold set of optional node manufacturing control parameters.

[0071] According to the previously configured adaptation algorithm set, the optional threshold range of each parameter to be optimized is input into the optimization algorithm as the initial condition, and the iterative optimization process is carried out. Specifically, the initial solution or initial population is generated, the optimization algorithm is run, the parameter combination is gradually adjusted, and the fitness value after each adjustment is evaluated using the fitness function constructed previously. When the maximum number of iterations is reached or the fitness value no longer changes significantly, the final parameter combination is determined as the optimization result. From the results of the optimization algorithm operation, the parameter combination with the highest fitness value is selected to form the node manufacturing optimization control parameter set.

[0072] Furthermore, the method comprises:

[0073] Perform correlation influence analysis on the metal mesh manufacturing process node set to obtain upstream and downstream node correlation influence parameters; perform influence correction on the optional node manufacturing control parameter threshold set based on the upstream and downstream node correlation influence parameters to determine the node manufacturing control parameter optimization threshold set; optimize and correct the node manufacturing optimization control parameter set based on the node manufacturing control parameter optimization threshold set.

[0074] Collect detailed data of all manufacturing process nodes, including operating parameters, production conditions, quality results, etc., and use statistical analysis, data mining and network analysis methods to analyze the correlation between manufacturing process nodes. For example, use the correlation analysis method to calculate the correlation coefficient between the parameters of each node and determine the mutual influence between the nodes; use the causal analysis method to determine the causal relationship between upstream and downstream nodes.

[0075] The correlation influence between upstream and downstream nodes is extracted to form the correlation influence parameters of upstream and downstream nodes, including direct influence parameters, such as the direct influence of temperature on downstream processes, and indirect influence parameters, such as the conduction influence of intermediate nodes.

[0076] According to the upstream and downstream node association influence parameters, the thresholds of the optional node manufacturing control parameters are corrected, that is, the influence of the upstream node parameter changes on the downstream nodes is considered and adjusted, for example, using a mathematical model or empirical formula to apply the association influence parameters to the correction of the optional node parameters. For example, if the upstream temperature change ΔT has an impact on the downstream pressure P, the correction formula can be P=P+k*ΔT, where k is the influence coefficient. A new node manufacturing control parameter optimization threshold set is formed according to the corrected optional node manufacturing control parameters.

[0077] The node manufacturing optimization control parameter set is optimized and modified using the modified node manufacturing control parameter optimization threshold set. That is, in the process of using the optimization algorithm, initial parameters are randomly generated within the threshold range according to the optimization threshold set. By using the modified node manufacturing control parameter optimization threshold set, the uncertainty in the manufacturing process can be eliminated or reduced, the consistency of production can be ensured, and the optimal parameter combination can be found, thereby improving the performance and quality of the product.

[0078] In summary, the manufacturing control method of a metal mesh provided in the embodiment of the present application has the following technical effects:

[0079] By obtaining the application scenario information of the target metal mesh and conducting a multi-dimensional demand factor analysis, specific manufacturing demand factor parameters are obtained to ensure that the manufactured metal mesh can fully meet the specific needs of different application scenarios and improve the applicability and performance of the product; manufacturing data mining is performed based on the metal mesh manufacturing process node set, and a manufacturing process node space set is constructed, which includes the historical manufacturing control parameters and effect data of each node, provides data support for manufacturing process optimization, and improves the reliability of the manufacturing process; based on the manufacturing process node space set, the metal mesh manufacturing demand factor parameters are optimized and analyzed to determine the node manufacturing control parameter set, achieve the global optimum of the manufacturing parameters, and improve the optimization effect of the manufacturing process and product quality; by performing quality feedback tuning on the node manufacturing control parameter set, the node manufacturing optimization control parameter set is obtained, and adaptive manufacturing control is performed based on this, which realizes the intelligent and flexible adjustment of the manufacturing process, and can adaptively adjust the process parameters according to the actual production situation to improve the production efficiency and the stability of product quality. Through the above method, the problems existing in the existing technology in the metal mesh manufacturing process, such as insufficient application scenario analysis, unintelligent manufacturing process, insufficient data utilization, and unsystematic optimization process, are comprehensively solved, the intelligence and flexibility of the manufacturing process are improved, and the overall improvement of product quality and production efficiency is achieved.

[0080] Based on the same inventive concept as the manufacturing control method of a metal mesh cloth in the aforementioned embodiment, Figure 2 As shown, an embodiment of the present application provides a metal mesh manufacturing control system, the system comprising:

[0081] A multidimensional demand factor analysis module 10, the multidimensional demand factor analysis module 10 is used to obtain application scenario information of the target metal mesh, perform multidimensional demand factor analysis on the application scenario information, and obtain metal mesh manufacturing demand factor parameters; a matching and screening module 20, the matching and screening module 20 is used to encode the metal mesh manufacturing demand factor parameters, generate metal mesh manufacturing factor encoding information, perform matching and screening from the metal mesh process chain based on the metal mesh manufacturing factor encoding information, and obtain a metal mesh manufacturing process node set; a manufacturing data mining module 30, the manufacturing data mining module 30 is used to perform manufacturing data mining based on the metal mesh manufacturing process node set, Construct a manufacturing process node space set, the manufacturing process node space set includes historical node manufacturing control parameters of each metal mesh manufacturing process node and corresponding node manufacturing effect data; an optimization analysis module 40, the optimization analysis module 40 is used to perform optimization analysis on the metal mesh manufacturing demand factor parameters based on the manufacturing process node space set, and determine the node manufacturing control parameter set; an adaptive manufacturing control module 50, the adaptive manufacturing control module 50 is used to perform quality feedback tuning on the node manufacturing control parameter set, obtain the node manufacturing optimization control parameter set, and perform adaptive manufacturing control on the target metal mesh based on the node manufacturing optimization control parameter set.

[0082] Furthermore, the system further includes a coding information generating module to perform the following operation steps:

[0083] Obtaining metal mesh demand factor information, the metal mesh demand factor information including specification size, mechanical properties, mesh surface flatness, mesh shape and aperture, and physical and chemical properties; classifying and filling the metal mesh demand factor information through a metal mesh knowledge base to obtain a metal mesh demand factor content set; using the metal mesh demand factor content set as a coding element, designing a coding sequence, coding length, and coding symbols; determining a metal mesh manufacturing coding rule based on the coding element and the coding sequence, coding length, and coding symbols; encoding the metal mesh manufacturing demand factor parameters using the metal mesh manufacturing coding rule to generate the metal mesh manufacturing factor coding information.

[0084] Furthermore, the system further includes a manufacturing process node set acquisition module to perform the following operation steps:

[0085] A metal mesh process database is obtained according to the metal mesh process chain, wherein the metal mesh process database includes process record data information of each manufacturing process node in the metal mesh process chain; the process record data information of each manufacturing process node is labeled with manufacturing requirements to obtain node manufacturing requirement label characteristic information; the node manufacturing requirement label characteristic information is encoded according to the metal mesh manufacturing coding rule to obtain a process chain node manufacturing requirement coding set; matching and screening are performed based on the metal mesh manufacturing factor coding information and the process chain node manufacturing requirement coding set to obtain a metal mesh manufacturing process node set.

[0086] Furthermore, the system further includes a control parameter set acquisition module to perform the following operation steps:

[0087] Obtain node parameter optimization factor information, wherein the node parameter optimization factor information includes optimization target complexity, node data characteristics, and allocated computing resources; perform multi-factor analysis on the manufacturing process node space set based on the node parameter optimization factor information to obtain a node adaptation parameter optimization algorithm set; map and configure the node adaptation parameter optimization algorithm set to the manufacturing process node space set, and initialize the optimization algorithm optimization parameters; use the metal mesh manufacturing demand factor parameters as optimization constraints, perform global optimization within the manufacturing process node space set based on the node adaptation parameter optimization algorithm set and the optimization algorithm optimization parameters, and determine the node manufacturing control parameter set.

[0088] Furthermore, the system further includes a control parameter set determination module to perform the following operation steps:

[0089] The parameters of the metal mesh manufacturing demand factor are used as optimization constraints, and are optimally divided with the manufacturing process node space set to obtain a manufacturing process node memory library set; effect indicators are extracted from the node manufacturing effect data to determine a node manufacturing effect evaluation indicator set, and the node manufacturing effect evaluation indicator set is fitted based on the manufacturing process node space set to construct a node manufacturing effect evaluation fitness function set; based on the node adaptation parameter optimization algorithm set and the optimization algorithm optimization parameters, iterative optimization is performed within the manufacturing process node memory library set, and parameter optimization comparison is performed through the node manufacturing effect evaluation fitness function set to determine the node manufacturing control parameter set.

[0090] Furthermore, the system further includes an optimization control parameter set acquisition module to perform the following operation steps:

[0091] The target metal mesh is subjected to quality inspection feedback through the node manufacturing control parameter set to obtain a metal mesh quality inspection data stream; the performance to be optimized is extracted from the metal mesh quality inspection data stream to determine a node quality performance parameter set to be optimized; based on the node quality performance parameter set to be optimized, the node manufacturing control parameter set is fine-tuned and optimized and analyzed to obtain an optional node manufacturing control parameter threshold set; the node adaptation parameter optimization algorithm set is used to respectively optimize the optional node manufacturing control parameter threshold sets to obtain the node manufacturing optimization control parameter set.

[0092] Furthermore, the system also includes an optimization and correction module to perform the following operation steps:

[0093] Perform correlation influence analysis on the metal mesh manufacturing process node set to obtain upstream and downstream node correlation influence parameters; perform influence correction on the optional node manufacturing control parameter threshold set based on the upstream and downstream node correlation influence parameters to determine the node manufacturing control parameter optimization threshold set; optimize and correct the node manufacturing optimization control parameter set based on the node manufacturing control parameter optimization threshold set.

[0094] Through the above-mentioned detailed description of the manufacturing control method of a metal mesh cloth in this specification, those skilled in the art can clearly know the manufacturing control system of a metal mesh cloth in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0095] 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.

Claims

1. A method for controlling the manufacture of a metal mesh, characterized in that: The method comprises: Acquire application scenario information of a target metal mesh, perform multi-dimensional demand factor analysis on the application scenario information, and obtain metal mesh manufacturing demand factor parameters; Encoding the metal mesh cloth manufacturing demand factor parameters to generate metal mesh cloth manufacturing factor encoding information, and performing matching screening from the metal mesh cloth process chain based on the metal mesh cloth manufacturing factor encoding information to obtain a metal mesh cloth manufacturing process node set; Based on the metal mesh cloth manufacturing process node set, manufacturing data mining is performed respectively to construct a manufacturing process node space set, wherein the manufacturing process node space set includes historical node manufacturing control parameters of each metal mesh cloth manufacturing process node and corresponding node manufacturing effect data; Based on the manufacturing process node space set, respectively, the metal mesh manufacturing demand factor parameters are optimized and analyzed to determine the node manufacturing control parameter set; Quality feedback tuning is performed on the node manufacturing control parameter set to obtain a node manufacturing optimization control parameter set, and adaptive manufacturing control is performed on the target metal mesh based on the node manufacturing optimization control parameter set.

2. A method for controlling the manufacture of a metal mesh cloth as claimed in claim 1, characterized in that: The generating of the metal mesh manufacturing factor coding information includes: Obtaining metal mesh demand factor information, wherein the metal mesh demand factor information includes specification size, mechanical properties, mesh surface flatness, mesh shape and aperture, and physical and chemical properties; Classify and fill the metal mesh demand factor information through the metal mesh knowledge base to obtain a metal mesh demand factor content set; Taking the metal mesh demand factor content set as coding elements, designing the coding sequence, coding length and coding symbol; Determine the metal mesh manufacturing coding rules based on the coding elements and the coding sequence, coding length and coding symbol; The metal mesh cloth manufacturing requirement factor parameters are encoded using the metal mesh cloth manufacturing encoding rules to generate the metal mesh cloth manufacturing factor encoding information.

3. A method for controlling the manufacture of a metal mesh cloth as claimed in claim 2, characterized in that: The step of obtaining a set of metal mesh manufacturing process nodes includes: Acquire a metal mesh cloth process database according to the metal mesh cloth process chain, wherein the metal mesh cloth process database includes process record data information of each manufacturing process node in the metal mesh cloth process chain; Labeling the process record data information of each manufacturing process node with manufacturing requirements to obtain node manufacturing requirement label characteristic information; Encode the node manufacturing requirement label characteristic information according to the metal mesh manufacturing coding rule to obtain a process chain node manufacturing requirement coding set; Based on the matching and screening of the metal mesh cloth manufacturing factor coding information and the process chain node manufacturing requirement coding set, a metal mesh cloth manufacturing process node set is obtained.

4. The manufacturing control method of a metal mesh cloth according to claim 1, characterized in that: The determining of the node manufacturing control parameter set includes: Obtaining node parameter optimization factor information, wherein the node parameter optimization factor information includes optimization target complexity, node data characteristics, and allocated computing resources; Performing multi-factor analysis on the manufacturing process node space set based on the node parameter optimization factor information to obtain a node adaptation parameter optimization algorithm set; Mapping and configuring the node adaptation parameter optimization algorithm set to the manufacturing process node space set, and initializing the optimization algorithm optimization parameters; The metal mesh manufacturing demand factor parameters are used as optimization constraints, and global optimization is performed in the manufacturing process node space set based on the node adaptation parameter optimization algorithm set and the optimization algorithm optimization parameters to determine the node manufacturing control parameter set.

5. A method for controlling the manufacture of a metal mesh cloth as claimed in claim 4, characterized in that: The determining the node manufacturing control parameter set includes: The metal mesh manufacturing demand factor parameters are used as optimization constraints, and are optimally divided with the manufacturing process node space set to obtain a manufacturing process node memory library set; Extracting effect indicators from the node manufacturing effect data, determining a node manufacturing effect evaluation indicator set, fitting the node manufacturing effect evaluation indicator set based on the manufacturing process node space set, and constructing a node manufacturing effect evaluation fitness function set; Based on the node adaptation parameter optimization algorithm set and the optimization algorithm optimization parameters, iterative optimization is performed in the manufacturing process node memory library set, and parameter optimization comparison is performed through the node manufacturing effect evaluation fitness function set to determine the node manufacturing control parameter set.

6. A method for controlling the manufacture of a metal mesh cloth as claimed in claim 4, characterized in that: The step of obtaining a set of node manufacturing optimization control parameters includes: Performing quality inspection feedback on the target metal mesh through the node manufacturing control parameter set to obtain a metal mesh quality inspection data stream; Extracting the performance to be optimized from the metal mesh quality detection data stream, and determining a set of node quality performance parameters to be optimized; Based on the node quality performance parameter set to be optimized, fine-tuning and optimizing the node manufacturing control parameter set is performed to obtain an optional node manufacturing control parameter threshold set; The node adaptation parameter optimization algorithm set is used to optimize the optional node manufacturing control parameter threshold sets respectively to obtain the node manufacturing optimization control parameter set.

7. A method for controlling the manufacture of a metal mesh cloth as claimed in claim 6, characterized in that: The method comprises: Performing correlation impact analysis on the metal mesh manufacturing process node set to obtain correlation impact parameters of upstream and downstream nodes; Based on the upstream and downstream node association influence parameters, the optional node manufacturing control parameter threshold set is modified to determine the node manufacturing control parameter optimization threshold set; The node manufacturing optimization control parameter set is optimized and corrected based on the node manufacturing control parameter optimization threshold set.

8. A metal mesh manufacturing control system, characterized in that: A method for manufacturing and controlling a metal mesh cloth according to any one of claims 1 to 7, the system comprising: A multi-dimensional demand factor analysis module, which is used to obtain application scenario information of a target metal mesh, perform multi-dimensional demand factor analysis on the application scenario information, and obtain metal mesh manufacturing demand factor parameters; A matching and screening module, wherein the matching and screening module is used to encode the metal mesh cloth manufacturing demand factor parameters, generate metal mesh cloth manufacturing factor encoding information, and perform matching and screening from the metal mesh cloth process chain based on the metal mesh cloth manufacturing factor encoding information to obtain a metal mesh cloth manufacturing process node set; A manufacturing data mining module, wherein the manufacturing data mining module is used to perform manufacturing data mining based on the metal mesh manufacturing process node set, and construct a manufacturing process node space set, wherein the manufacturing process node space set includes historical node manufacturing control parameters of each metal mesh manufacturing process node and corresponding node manufacturing effect data; An optimization analysis module, the optimization analysis module is used to perform optimization analysis on the metal mesh manufacturing demand factor parameters based on the manufacturing process node space set, and determine the node manufacturing control parameter set; An adaptive manufacturing control module is used to perform quality feedback tuning on the node manufacturing control parameter set, obtain a node manufacturing optimization control parameter set, and perform adaptive manufacturing control on the target metal mesh based on the node manufacturing optimization control parameter set.

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