Engineering material extrusion control system based on performance constraint

Abstract

The invention discloses an engineering material extrusion control system based on performance constraint, and relates to the related technical field of engineering manufacturing, and the system comprises the following steps: obtaining performance analysis data of an engineering material; performing data stability probability analysis, and determining constraint performance based on the data trend stability probability; performing support degree relation operation of decomposition parameters by using a process target result to obtain a process core control parameter; establishing a mapping relation between the constraint performance and the flow core control parameters; and optimizing control parameters when the control target is maximum to obtain extrusion control information, and sending the extrusion control information to a control center to perform extrusion operation process control. The technical problems of unstable product quality and low production efficiency caused by difficulty in real-time monitoring and accurate control of key parameters in the extrusion process in the existing engineering material extrusion control are solved, automatic adjustment of extrusion parameters is realized, and the technical effects of improving the stability of the product quality and the production efficiency are achieved.

Description

Technical Field

[0001] This application relates to the field of engineering manufacturing technology, and specifically relates to an extrusion control system for engineering materials based on performance constraints. Background Art

[0002] The extrusion control system for engineering materials is one of the key technologies in the field of modern industrial manufacturing. It plays a crucial role especially in the processing of engineering materials such as plastics, rubbers, and composite materials. With the continuous development of industrial manufacturing technology, the requirements for the extrusion control system of engineering materials are getting higher and higher. In actual production applications, the extrusion process of engineering materials involves multiple complex variables, which directly affect the product quality and production efficiency. Traditional extrusion control systems often fail to achieve precise control of key parameters such as material flow rate, temperature, and pressure during the extrusion process, resulting in unstable product quality and low production efficiency, and it is difficult to meet the needs of modern industry for high-efficiency and high-quality production.

[0003] Therefore, in the current technologies related to the extrusion control of engineering materials, there are technical problems such as difficulty in real-time monitoring and precise control of key parameters during the extrusion process, which in turn lead to unstable product quality and low production efficiency. Summary of the Invention

[0004] This application provides an extrusion control system for engineering materials based on performance constraints. By using technical means such as parameter decomposition operation, establishing mapping relationships, and backtracking search optimization, it solves the technical problems existing in the existing extrusion control of engineering materials, such as difficulty in real-time monitoring and precise control of key parameters during the extrusion process, which in turn lead to unstable product quality and low production efficiency, and realizes the automatic adjustment of extrusion parameters, achieving the technical effects of improving the stability of product quality and production efficiency.

[0005] This application provides an extrusion control system for engineering materials based on performance constraints. The system includes: a performance analysis data acquisition module, which is used to connect to a performance analysis platform and obtain performance analysis data of engineering materials, including material viscosity, temperature-fluidity, pressure deformation relationship, and material composition performance relationship; a constraint performance determination module, which is used to perform data stability probability analysis based on the performance analysis data and determine the constraint performance based on the data trend stability probability. The constraint performance is a performance parameter whose data trend stability probability exceeds a preset range; a decomposition parameter operation module, which is used to decompose the parameters of the extrusion control process and perform an operation on the support degree relationship of the decomposition parameters using the process target result to obtain the core control parameters of the process. The core control parameters of the process are process control parameters whose support degree meets the screening requirements; a mapping relationship establishment module, which is used to establish a mapping relationship between the constraint performance and the core control parameters of the process and configure process optimization variables; an extrusion operation process control module, which is used to optimize the control parameters based on the process optimization variables with the maximum control target to obtain extrusion control information and send it to the control center for extrusion operation process control.

[0006] In a possible implementation, the extrusion operation process control module also performs the following processing: based on the process optimization variables, set search constraint conditions to search for a control parameter sample set, and randomly generate a first population and a second population; according to the first population and the second population, determine the updated search center and the updated search direction, where the updated search center is determined by the magnitude of a random number; using the updated search center and the updated search direction, perform mutation on the process variable control scheme according to a set step size to obtain a mutated population, exchange the individual elements in the second population with the elements in the same position in the mutated population to generate a new sample. When the evaluation value of the new sample exceeds the evaluation value of the individual elements in the second population, perform population update; when the evaluation value of the new sample is lower than the evaluation value of the individual elements in the second population, eliminate the new sample and mark the corresponding individual sample for termination to indicate that the search update is prohibited; repeat the population update iteratively until the optimization stop target is reached, and obtain the process variable control scheme with the optimal evaluation value as the extrusion control information.

[0007] In a possible implementation, the performance analysis data acquisition module further performs the following processing: constructing a twin simulation module and inserting it into the performance analysis platform, and through the twin simulation module, based on temperature, pressure, and material composition ratio as independent variables respectively, testing target quantities such as material viscosity, fluidity, deformation amount, and material stability, and recording simulation test data; respectively performing data fitting on the independent variables and target quantities to obtain influence relationships; establishing mapping relationships among the influence relationships, independent variables, and target quantities, and constructing the performance analysis data of the engineering materials.

[0008] In a possible implementation, the performance analysis data acquisition module further performs the following processing: based on the adjustment step of the independent variable and the influence relationship, performing stable probability analysis to determine the stable trend of the data trend; performing trend segmentation based on the stable trend of the data trend, including a regular trend region and an abnormal dive region, where the regular trend region is a stable development region matching the influence relationship, and the abnormal dive region is a region where the development trend direction changes and does not match the influence relationship; determining the constraint performance according to the segmentation nodes of the regular trend region and the abnormal dive region.

[0009] In a possible implementation, the decomposition parameter operation module further performs the following processing: obtaining the control parameters of the extrusion control process and the process target result, where the control parameters are the parameters that the control center can adjust and control, and the process target result is the target parameter used to evaluate the extrusion quality of the process node; based on the control parameters of the extrusion control process and the process target result, performing influence relationship analysis to obtain the influence relationship coefficient; based on the influence relationship coefficient, obtaining the support degree, setting screening requirements, and taking the control parameters whose support degree meets the screening requirements as the core control parameters of the process.

[0010] In a possible implementation, the decomposition parameter operation module further performs the following processing: based on the control parameters of the extrusion control process and the process target result, extracting the historical case data set; setting a search accuracy constraint value, searching from the historical case data set to determine the target case data set; based on the target case data set as the clustering center, performing hierarchical clustering to obtain multi-layer clustering clusters, and respectively fitting the relationship parameters of each layer of clustering clusters; setting a deviation threshold, and stopping clustering when the deviation value of the multi-layer relationship parameters reaches the deviation threshold; configuring multi-layer weights, and performing average weighted calculation of the multi-layer relationship coefficients to obtain the influence relationship coefficient, where the relationship parameter weight of the target case data set is the largest.

[0011] In a possible implementation, the decomposition parameter operation module further performs the following processing: performing a silhouette coefficient operation on the multi-layer clustering clusters to obtain the silhouette coefficients of each layer; performing a decentralization process based on all the silhouette coefficients of each layer to obtain the coefficient mean value; calculating a deviation value by using the silhouette coefficients of each layer and the coefficient mean value to determine an adjustment coefficient, and reconstructing the multi-layer weights by using the adjustment coefficient.

[0012] In a possible implementation, the decomposition parameter operation module further performs the following processing: calculating a deviation value by using the silhouette coefficients of each layer and the coefficient mean value to determine an adjustment coefficient, including, according to the formula: , where is the adjustment coefficient of the i-th layer, is the silhouette coefficient of the i-th layer, k is the coefficient mean value, and i is the clustering layer number.

[0013] It is intended to connect to a performance analysis platform through the engineering material extrusion control system based on performance constraints proposed in this application to obtain performance analysis data of engineering materials; perform data stability probability analysis according to the performance analysis data, and determine the constrained performance based on the data trend stability probability; decompose the parameters of the extrusion control process, perform a support degree relationship operation on the decomposition parameters by using the process target results to obtain the process core control parameters; establish a mapping relationship between the constrained performance and the process core control parameters, and configure process optimization variables; optimize the control parameters with the maximum control target based on the process optimization variables to obtain extrusion control information and send it to the control center for controlling the extrusion operation process. This solves the technical problems existing in the existing engineering material extrusion control, such as the difficulty in real-time monitoring and accurately controlling the key parameters in the extrusion process, which in turn leads to unstable product quality and low production efficiency, realizes the automatic adjustment of extrusion parameters, and achieves the technical effects of improving the stability of product quality and production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the systems according to the embodiments of the application. It should be understood that the operations in the front or below do not necessarily need to be executed precisely in sequence. On the contrary, according to the need, they can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several operations can be removed from these processes.

[0015] Figure 1 FIG. is a schematic structural diagram of the engineering material extrusion control system based on performance constraints provided by an embodiment of the present application; Figure 2Schematic diagram of the execution process of the performance analysis data acquisition module in the engineering material extrusion control system based on performance constraints provided by the embodiments of the present application; Figure 3 Schematic diagram of the execution process of the decomposition parameter operation module in the engineering material extrusion control system based on performance constraints provided by the embodiments of the present application.

[0016] Explanation of reference numerals: Performance analysis data acquisition module 10, Constraint performance determination module 20, Decomposition parameter operation module 30, Mapping relationship establishment module 40, Extrusion operation process control module 50. Detailed implementation manners

[0017] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically gives the detailed implementation manners of the present application.

[0018] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0019] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The terms "first\second" involved are only used to distinguish similar objects and do not represent a specific order for the objects. The terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, system, product or server including a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of the present application. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0020] The embodiments of the present application provide an engineering material extrusion control system based on performance constraints, as Figure 1 shown, the system includes: Performance analysis data acquisition module 10, the performance analysis data acquisition module 10 is used to connect to the performance analysis platform to obtain performance analysis data of engineering materials, including material viscosity, temperature-fluidity, pressure-deformation relationship, and material composition-performance relationship. By connecting to the performance analysis platform and obtaining the performance analysis data of engineering materials, where the performance analysis platform refers to a tool for analyzing and evaluating the performance of engineering materials, usually integrating multiple technologies to collect, analyze, and visualize material performance data. Specifically, after connecting to the performance analysis platform, users can obtain the above-mentioned performance analysis data by inputting specific material information and test conditions. Material viscosity refers to the internal friction property of engineering materials (fluids), that is, the magnitude of the internal resistance when the material flows. In engineering materials, viscosity affects the fluidity and processing performance of the material. Through the performance analysis platform, it provides data support for the adjustment and control of the production process; the temperature-fluidity relationship describes the flow performance of the material at different temperatures. The change in temperature will directly affect the molecular movement and viscosity of the material, thereby affecting its processing and forming performance. The performance analysis platform can monitor the impact of temperature changes on the fluidity of the material in real time to help engineers optimize the processing temperature and improve production efficiency; the pressure-deformation relationship reflects the degree of deformation of the material when subjected to external forces, which is crucial for predicting the performance of the material in actual use. The performance analysis platform measures the deformation of engineering materials by applying different pressures to provide detailed data for material mechanical property analysis; the material composition-performance relationship refers to the influence of each component in the material on its performance. Different component combinations will result in differences in the physical, chemical, and mechanical properties of the material. The performance analysis platform can reveal the relationship between them by analyzing the chemical composition, microstructure, and performance data of the material, facilitating subsequent quality control of engineering materials.

[0021] Constraint performance determination module 20, which is used to perform data stability probability analysis based on the performance analysis data and determine the constraint performance based on the data trend stability probability. The constraint performance is the performance parameter for which the data trend stability probability exceeds the preset range. Statistical methods are used to perform data stability probability analysis on the obtained engineering material performance analysis data to evaluate the stability of its trend, including analyzing the distribution of data points, change trends, fluctuation ranges, etc., to judge the stability and reliability of the material performance analysis data. According to the analysis results of the data stability probability, the performance parameters for which the data trend stability probability exceeds the preset range are identified, which are used to judge whether the performance parameters are in an ideal stable state. For example, when the data trend stability probability of a certain performance parameter is lower than the preset minimum stability probability, it means that the data of this parameter fluctuates greatly and is unstable, which may affect the processing quality or service performance of the material and is regarded as a constraint performance. Similarly, if the data trend stability probability of a certain performance parameter is higher than the preset maximum stability probability, it may also mean that the data is too stable and lacks the necessary flexibility or adaptability. The determination of the constraint performance helps to identify the key parameters that need to be particularly controlled during the material extrusion process. By optimizing and improving the constraint performance, the processing quality, service performance, and production stability and reliability of engineering materials can be improved.

[0022] Decomposition parameter operation module 30, which is used to decompose the parameters of the extrusion control process and perform the support degree relationship operation of the decomposition parameters by using the process target result to obtain the process core control parameters. The process core control parameters are the process control parameters whose support degree meets the screening requirements. Decompose the parameters of the extrusion control process and perform the support degree relationship operation of the decomposition parameters by using the process target result to obtain the process core control parameters. Specifically, conduct a detailed analysis of each key step and link in the extrusion process, disassemble and classify the various parameters involved, including the parameters in each stage such as raw material preparation, extrusion molding, and cooling sizing, for example, temperature, pressure, flow rate, cooling rate, etc. Use the process target result to perform the support degree relationship operation of the decomposition parameters, that is, quantify and evaluate these parameters, and calculate the contribution degree and influence degree of each parameter to achieve these goals according to the expected extrusion finished product quality, production efficiency, etc. Through the support degree relationship operation of the decomposition parameters, the parameters that have a significant impact on the achievement of the process target can be identified, that is, the process core control parameters. The process core control parameters not only have a support degree that meets the screening requirements but also have high controllability and adjustability in actual operation. By adjusting these parameters, the extrusion control process can be effectively optimized, and the product quality and production efficiency can be improved.

[0023] Mapping relationship establishment module 40 is configured to establish the mapping relationship between the constraint performance and the core process control parameters, and configure process optimization variables. Associate the determined constraint performance with the core process control parameters to clarify their mutual influence and corresponding relationship. Select and set the variables for optimizing the process according to actual needs. Usually, those parameters that have an important impact on the constraint performance and are easy to adjust and control in actual operation are the process optimization variables, which can better understand and control the entire extrusion process. Specifically, analyze the relationship between the constraint performance and the core process control parameters, and present it in the form of a mapping table or a mathematical model. When a certain constraint performance changes, the core process control parameters related to it can be quickly found, and the corresponding adjustment strategy can be determined. According to the mapping relationship, identify the core process control parameters with great potential for improving the constraint performance, analyze the adjustment range and limiting conditions of these parameters, determine their reasonable value range, set these parameters as process optimization variables, and formulate corresponding adjustment strategies and optimization goals. By configuring the process optimization variables, the extrusion process can be improved more pertinently, and the quality and performance of the product can be improved.

[0024] Extrusion operation process control module 50 is configured to optimize the control parameters based on the process optimization variables with the control target maximization, obtain extrusion control information, and send it to the control center for extrusion operation process control. Utilize the previously determined process optimization variables, and optimize the control parameters of the extrusion process with the maximization of the control target (such as improving product quality, improving production efficiency, reducing energy consumption, etc.) as the guidance, obtain extrusion control information, and input it to the control center to realize the extrusion operation process control. Among them, the control center is the core command system of the extrusion operation process, responsible for receiving and executing these control information. After receiving the extrusion control information, it automatically adjusts the operating parameters of the extrusion equipment to ensure that the extrusion process proceeds according to the optimized parameters. Through this process, the refined control of the extrusion operation process can be realized, the production cost and energy consumption can be reduced, and the product quality and production efficiency can be improved.

[0025] The engineering material extrusion control system based on performance constraints according to the embodiment of the present invention is used to solve the technical problems existing in the existing engineering material extrusion control, such as the difficulty in real-time monitoring and accurately controlling the key parameters in the extrusion process, which leads to unstable product quality and low production efficiency. It realizes the automatic adjustment of the extrusion parameters and achieves the technical effects of improving the stability of the product quality and the production efficiency. The engineering material extrusion control system based on performance constraints includes: performance analysis data acquisition module 10, constraint performance determination module 20, decomposition parameter operation module 30, mapping relationship establishment module 40, and extrusion operation process control module 50.

[0026] Next, the specific configuration of the performance analysis data acquisition module 10 will be described in detail. The performance analysis data acquisition module 10 may further include: inserting a twin simulation module into the performance analysis platform, and based on the twin simulation module, taking temperature, pressure, and material composition ratio as independent variables respectively, testing the target quantities of material viscosity, fluidity, deformation amount, and material stability, and recording the simulation test data. By using digital twin and simulation technologies, a twin simulation module is constructed to conduct a more comprehensive and accurate analysis of the performance of engineering materials. The twin simulation module can test the target quantities such as material viscosity, fluidity, deformation amount, and material stability based on the set independent variables (such as temperature, pressure, material composition ratio, etc.), so as to obtain the simulation test data. Specifically, the twin simulation module will simulate the extrusion process of the material in a virtual environment according to the input parameters such as temperature, pressure, and material composition ratio, and record and analyze the performance of the material under different conditions in real time. By adjusting these independent variables, the change trend of the material performance can be observed and analyzed, so as to find the optimal material formula and process parameters, which are recorded as the simulation test data. Data fitting is performed on the independent variables and the target quantities respectively to obtain the influence relationship. Data fitting is performed on the independent variables (including temperature, pressure, material composition ratio, etc.) and the target quantities (including material viscosity, fluidity, deformation amount, material stability, etc.) respectively to obtain the influence relationship between them. Specifically, through data fitting, the mathematical relationship between the independent variables and the target quantities is obtained, which helps to predict the change trend of the target quantities under different independent variable conditions. Establish the mapping relationship between the influence relationship, independent variables, and target quantities, and construct the performance analysis data of the engineering materials. Establishing the mapping relationship between the influence relationship, independent variables, and target quantities means associating the influence relationship, independent variables, and target quantities in a clear way. Constructing the performance analysis data of the engineering materials means integrating all the above information to form a comprehensive and systematic data set, including the original experimental data, the obtained influence relationship by fitting, the mapping relationship, and the resulting performance analysis results.

[0027] Next, the specific configuration of the constraint performance determination module 20 will be described in detail. The constraint performance determination module 20 further includes: performing a stable probability analysis based on the adjustment step size and influence relationship of the independent variable to determine the stable trend of the data trend. Performing a stable probability analysis based on the adjustment step size and influence relationship of the independent variable to determine the stable trend of the data trend, where the adjustment step size of the independent variable refers to the increment or decrement used when changing the independent variable (such as temperature, pressure, material composition ratio, etc.) in the experiment. The size of the adjustment step directly affects the accuracy and resolution of the experimental data. If the step size is too large, the data changes may be too drastic to analyze; if the step size is too small, it may increase the experimental cost and time. Specifically, using these influence relationships and the adjustment step size of the independent variable, a series of data points are obtained, and a stable probability analysis is performed on them to calculate the stable probability of the data trend. The stable probability refers to the probability that the data points fluctuate within a certain range without significant changes, which reflects the stability and reliability of the data trend. Based on the stable trend of the data trend, trend segmentation is performed, including a regular trend area and an abnormal dive area. The regular trend area is a stable development area that matches the influence relationship, and the abnormal dive area is an area where the development trend direction changes and does not match the influence relationship. Trend segmentation based on the stable trend of the data trend is an important step in data analysis, used to divide the overall trend into different regions or stages. Specifically, trend segmentation of the stable trend of the data trend can be performed through moving average, filtering algorithms, or clustering analysis, etc. The regular trend area refers to an area that matches the known influence relationship and shows stable development characteristics; the abnormal dive area refers to an area where the development trend direction changes significantly and does not match the influence relationship. According to the segmentation nodes of the regular trend area and the abnormal dive area, the constraint performance is determined. The segmentation nodes of the regular trend area and the abnormal dive area are the key points where the data trend changes significantly. Within the regular trend area, the data develops stably according to the expected influence relationship; while the abnormal dive area indicates that the data trend has changed unexpectedly. By analyzing the positions, characteristics of these segmentation nodes, as well as the changes in the corresponding independent variables and target quantities, the specific parameters and conditions of the constraint performance can be determined. The constraint performance usually refers to the performance limit or restriction that engineering materials can achieve under specific conditions. For example, in the test of material strength, the segmentation node may correspond to the point where the material starts to break, thereby determining the maximum bearing capacity of the material.

[0028] Next, the specific configuration of the decomposition parameter operation module 30 will be described in detail. The decomposition parameter operation module 30 further includes: obtaining the control parameters of the extrusion control process and the process target result, where the control parameters are the parameters that the control center can adjust and control, and the process target result is the target parameter for evaluating the extrusion quality of the process node. By obtaining and controlling these key parameters, the control center can achieve precise regulation of the extrusion process, thereby ensuring that the product quality and production efficiency reach the optimal state. The real-time monitoring and evaluation of the process target result also help to detect and solve problems in a timely manner, further improving the stability and reliability of the production process. Based on the control parameters of the extrusion control process and the process target result, an influence relationship analysis is performed to obtain an influence relationship coefficient. Among them, the influence relationship coefficient represents the degree of influence between the control parameter and the process target result. The positive or negative of the influence relationship coefficient can tell us whether the parameter has a positive or negative impact on the result, and the magnitude of the coefficient reflects the strength of the influence. Based on the influence relationship coefficient, the support degree is obtained, and screening requirements are set. The control parameters whose support degree meets the screening requirements are used as the core control parameters of the process. These influence relationship coefficients are used to calculate the support degree, which is used to measure the importance of a certain control parameter in influencing the process target result. Specifically, the support degree can be calculated based on the absolute value or relative value of the influence relationship coefficient to reflect the overall influence degree of the parameter on the target result. After obtaining the support degree, we need to set screening requirements, and multiple factors should be considered, including the adjustability of the parameter, the degree of influence on the target result, and the cost required for implementation control. For example, a threshold of the support degree is set, and only the control parameters that exceed this threshold will be considered to meet the support degree. The control parameters whose support degree meets the screening requirements are used as the core control parameters of the process.

[0029] Next, the specific configuration of the decomposition parameter operation module 30 will be further described in detail. The decomposition parameter operation module 30 further includes: extracting a historical case data set based on the control parameters of the extrusion control process and the process target result. Collect and organize relevant data records from past extrusion production processes according to the control parameters of the extrusion control process and the process target result. These data records the process target results obtained under different control parameter settings. Use these historical data to analyze and understand the relationship between control parameters and process target results, providing data support for optimizing the extrusion control process. Set a search accuracy constraint value and search in the historical case data set to determine the target case data set. The search constraint value can be a specific numerical value or a specific condition, reflecting our requirements for the accuracy of the target case. According to the set search accuracy constraint value, search in the historical case data set, and multiple historical cases that meet the search accuracy constraint value form the target case data set. Based on the target case data set as the clustering center, perform hierarchical clustering to obtain multi-layer clustering clusters, and respectively fit the relationship parameters of each layer of clustering clusters. Use the target case data set as the center of clustering to perform hierarchical clustering, calculate the similarity or distance between cases, and gradually merge or split the cases into different clusters according to these similarities or distances. As the clustering progresses, multi-layer clustering clusters will be formed, and each layer represents different levels of clustering results. Analyze the data within each layer of clustering clusters to discover the relationships or patterns between them. By fitting these relationship parameters, we can better understand the internal structure and characteristics of the data. Set a deviation threshold, and when the deviation value of the multi-layer relationship parameters reaches the deviation threshold, stop clustering. Set a deviation threshold, and when the deviation value of the multi-layer relationship parameters reaches this threshold, stop clustering, which is an evaluation of the stability of the clustering result and a control of the clustering accuracy. Among them, the deviation threshold is a preset numerical value used to quantify the change or difference degree of the multi-layer relationship parameters. By comparing the differences and change rates of the relationship parameters at different levels, calculate the deviation value of the relationship parameters between adjacent levels. When this deviation value reaches or exceeds the set deviation threshold, it is considered that the clustering result has shown a large instability or change, and continuing clustering may lead to problems such as overfitting. Stop clustering to ensure the stability and consistency of the clustering result and avoid an overly complex clustering structure. Configure multi-layer weights and perform weighted average calculation of the multi-layer relationship coefficients to obtain the influence relationship coefficient. Among them, the relationship parameter weight of the target case data set is the largest. Different weights are assigned to the clustering clusters at different levels and their relationship parameters. By means of weighted average, calculate the multi-layer relationship coefficients to obtain the influence relationship coefficient. Among them, the relationship parameters of the target case data set are assigned the largest weight to emphasize their importance in the calculation.

[0030] Next, the specific configuration of the decomposition parameter operation module 30 will be further described in detail. The decomposition parameter operation module 30 further includes: calculating the silhouette coefficient for each layer of the multi-layer clustering clusters to obtain the silhouette coefficients of each layer. During the hierarchical clustering process, the silhouette coefficient evaluation method is applied to the clustering results of each layer, so as to obtain the silhouette coefficient values of the clustering results of each layer. The silhouette coefficient is an index for measuring the clustering effect. It can comprehensively consider the compactness and separation degree of clustering. For each sample point, the silhouette coefficient ( ) can be calculated by the following formula: ; where, is the average distance between the sample point and other points within the same cluster, is the minimum average distance between the sample point and all points in other clusters. Based on all the silhouette coefficients of each layer, a de-centralization process is performed to obtain the coefficient mean. Using the silhouette coefficients of each layer and the coefficient mean to calculate the deviation value, an adjustment coefficient is determined, and the multi-layer weights are reconstructed using the adjustment coefficient. Calculate the deviation value between the silhouette coefficient of each layer and the coefficient mean. According to the calculated deviation value, an adjustment coefficient can be determined. For example, a layer with a larger deviation value may be assigned a larger adjustment coefficient. Using the determined adjustment coefficient, the original multi-layer weights are reconstructed, that is, by multiplying the weight of each layer by the corresponding adjustment coefficient. By reconstructing the weights, those layers with better clustering effects can obtain larger weights in the subsequent weighted calculation, thereby improving the accuracy and reliability of the overall clustering result.

[0031] Next, the specific configuration of the decomposition parameter operation module 30 will be further described in detail. The decomposition parameter operation module 30 can further include: using the silhouette coefficients of each layer and the coefficient mean to calculate the deviation value to determine the adjustment coefficient, including: according to the formula: , where, is the adjustment coefficient of the i-th layer, is the silhouette coefficient of the i-th layer, k is the coefficient mean, and i is the clustering layer number.

[0032] Next, the specific configuration of the extrusion operation process control module 50 will be described in detail. The extrusion operation process control module 50 further includes: setting search constraint conditions based on the process optimization variables to search for a control parameter sample set, and randomly generating a first population and a second population; determining an updated search center and an updated search direction according to the first population and the second population, wherein the updated search center is determined by the magnitude of a random number; using the updated search center and the updated search direction to perform mutation on the process variable control scheme according to a set step size to obtain a mutated population, exchanging the individual elements in the second population with the elements at the same positions in the mutated population to generate a new sample, and performing population update when the evaluation value of the new sample exceeds the evaluation value of the individual elements in the second population; when the evaluation value of the new sample is lower than the evaluation value of the individual elements in the second population, eliminating the new sample and marking the corresponding individual sample for termination, which is used to identify the prohibition of updating the search; repeating the population update iteratively until the optimization stop target is reached, and obtaining the process variable control scheme with the optimal evaluation value as the extrusion control information. The process of setting search constraint conditions based on the process optimization variables to search for a control parameter sample set and then performing a series of optimization operations mainly uses the backtracking search optimization algorithm to find the optimal process variable control scheme. Specifically, after setting the search constraint conditions, a first population and a second population are randomly generated. According to the characteristics of the first population and the second population, the center point and direction of the search are determined by random numbers, which increases the randomness and global nature of the search. The search direction determines the direction in which to search in the search space to determine the updated search center and search direction; mutating the process variable control scheme according to the set step size to generate new solutions in the search space according to certain rules, that is, new process variable control schemes, namely adjusting parameters to generate new process variable control schemes, and then performing crossover and mutation operations, exchanging the individual elements in the second population with the elements at the same positions in the mutated population to generate a new sample; evaluating the new sample and calculating its evaluation value. When the evaluation value of the new sample exceeds the evaluation value of the individual elements in the second population, population update is performed with the optimal solutions in the second population and the mutated population; when the evaluation value of the new sample is lower than the evaluation value of the individual elements in the second population, the new sample is eliminated and the corresponding individual sample is marked for termination, indicating that this search direction will no longer be considered in subsequent iterations. The population update is repeated iteratively until the optimization stop target is reached, that is, when a certain stop condition is met (such as reaching a preset number of iterations, the evaluation value of the optimal individual in the population remains stable and does not change, etc.), the iteration is stopped, and the individual with the optimal evaluation value in the population is the required process variable control scheme, which is used as the final extrusion control information.

[0033] Although the present application makes various references to certain modules in the system according to embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or the server. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0034] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. The engineering material extrusion control system based on performance constraints is characterized by: The system comprises: A performance analysis data acquisition module, which is used to connect to the performance analysis platform to obtain performance analysis data of engineering materials, including material viscosity, temperature-fluidity, pressure-deformation relationship, and material composition-performance relationship; A constraint performance determination module, the constraint performance determination module is used to perform data stability probability analysis according to the performance analysis data, and determine the constraint performance based on the data trend stability probability, wherein the constraint performance is a performance parameter where the data trend stability probability exceeds a preset range; A decomposition parameter operation module, which is used to perform parameter decomposition on the extrusion control process, perform support relationship operation on the decomposition parameters using the process target results, and obtain the process core control parameters, which are process control parameters whose support meets the screening requirements; A mapping relationship establishment module, the mapping relationship establishment module is used to establish a mapping relationship between the constraint performance and the process core control parameters, and configure process optimization variables; The extrusion operation process control module is used to optimize the control parameters based on the process optimization variables to maximize the control target, obtain extrusion control information, and send it to the control center to control the extrusion operation process.

2. The engineering material extrusion control system based on performance constraints as claimed in claim 1, characterized in that: The extrusion operation process control module performs the following steps: Based on the process optimization variables, search constraints are set to search for control parameter sample sets, and a first population and a second population are randomly generated; Determine an update search center and an update search direction according to the first population and the second population, wherein the update search center is determined by the size of a random number; The process variable control scheme is mutated according to the set step length by updating the search center and the search direction to obtain a mutant population, and the individual elements in the second population are exchanged with the elements at the same position in the mutant population to generate a new sample. When the evaluation value of the new sample exceeds the evaluation value of the individual element of the second population, the population is updated; When the evaluation value of the new sample is lower than the evaluation value of the individual element of the second population, the new sample is eliminated, and the corresponding individual sample is marked as terminated to indicate that the update search is prohibited; The population is updated repeatedly until the optimization stop target is reached, and the process variable control scheme with the best evaluation value is obtained as the extrusion control information.

3. The engineering material extrusion control system based on performance constraints as claimed in claim 1, characterized in that: The performance analysis data acquisition module performs the following steps: Constructing a twin simulation module and inserting it into the performance analysis platform, and using the twin simulation module to perform target quantity tests on material viscosity, fluidity, deformation, and material stability based on temperature, pressure, and material composition ratio as independent variables, and recording simulation test data; Perform data fitting of independent variables and target quantities respectively to obtain the influence relationship; A mapping relationship among the influencing relationship, independent variable, and target quantity is established to construct performance analysis data of the engineering material.

4. The engineering material extrusion control system based on performance constraints as claimed in claim 3, characterized in that: The constraint performance determination module performs the following steps: Based on the adjustment step size and influence relationship of the independent variables, a stability probability analysis is performed to determine the stability trend of the data; Based on the stable trend of the data trend, trend segmentation is performed, including a regular trend area and an abnormal dive area. The regular trend area is a stable development area that matches the influence relationship, and the abnormal dive area is an area where the development trend direction does not match the influence relationship and changes; The constraint performance is determined according to the segmentation nodes of the regular trend area and the abnormal diving area.

5. The engineering material extrusion control system based on performance constraints as claimed in claim 1, characterized in that: The decomposition parameter operation module performs the following steps: Obtaining control parameters and process target results of the extrusion control process, wherein the control parameters are parameters that can be adjusted and controlled by the control center, and the process target results are target parameters for evaluating the extrusion quality of the process nodes; Based on the control parameters of the extrusion control process and the process target results, an influence relationship analysis is performed to obtain an influence relationship coefficient; Based on the influence relationship coefficient, the support degree is obtained, screening requirements are set, and the control parameters whose support degrees meet the screening requirements are used as the core control parameters of the process.

6. The engineering material extrusion control system based on performance constraints as claimed in claim 5, characterized in that: The decomposition parameter operation module performs the following steps: extracting a historical case data set based on the control parameters of the extrusion control process and the process target results; Setting a search precision constraint value, searching from the historical case data set, and determining a target case data set; Based on the target case data set as the cluster center, hierarchical clustering is performed to obtain multi-layer clusters, and the relationship parameters of each layer of clusters are fitted respectively; Setting a deviation threshold, when the deviation value of the multi-layer relationship parameter reaches the deviation threshold, stopping clustering; Multi-layer weights are configured, and multi-layer relationship coefficients are averagely weighted calculated to obtain the influencing relationship coefficient, wherein the relationship parameter weight of the target case data set is the largest.

7. The engineering material extrusion control system based on performance constraints as claimed in claim 6, characterized in that: The system further comprises: Perform silhouette coefficient calculation on multi-layer clustering to obtain the silhouette coefficient of each layer; Decentralize the contour coefficients of all layers and obtain the mean value of the coefficients; Deviation calculation is performed using the contour coefficients of each layer and the coefficient mean to determine an adjustment coefficient, and the multi-layer weights are reconstructed using the adjustment coefficient.

8. The engineering material extrusion control system based on performance constraints as claimed in claim 7, characterized in that: Calculating the deviation value by using the profile coefficients of each layer and the coefficient mean value to determine the adjustment coefficient includes: According to the formula: ,in, is the adjustment coefficient of the i-th layer, is the silhouette coefficient of the i-th layer, k is the mean of the coefficient, and i is the number of clustering layers.

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