Stamping process parameter optimization method and device, electronic equipment and medium

Abstract

The invention relates to a stamping process parameter optimization method and device, electronic equipment and a medium, and belongs to the technical field of stamping. The method comprises the steps that a data set composed of sheet forming quality parameters under different stamping process parameters and total carbon emission data in the stamping machining process is obtained; fitting the data set to obtain an approximate model of forming quality parameters and total carbon emission predicted according to the stamping process parameters; and based on the approximation model and a multi-target optimization algorithm, multi-target optimization is carried out with the optimal forming quality parameter and the minimum total carbon emission as targets, and an optimal process parameter set is obtained. According to the method, multi-target optimization is carried out by taking the optimal forming quality parameter and the minimum total carbon emission as targets through the approximation model, the optimal stamping process parameter combination is obtained, and the problem of high carbon emission in the low-carbon steel stamping process is effectively solved on the premise that the forming quality of the stamping process is met.

Description

Technical Field

[0001] The present application relates to the field of stamping technology, and in particular to a stamping process parameter optimization method, device, electronic equipment and medium. Background Art

[0002] Industry has significant high energy consumption and high emission characteristics and is the main source of greenhouse gases. As a typical labor-intensive and technology-intensive industry, the shipbuilding industry has the characteristics of complex process flow, long construction cycle, and large number of energy consumption types. It generally has "high carbonization" problems such as difficult to control greenhouse gas increments and low energy utilization efficiency. Therefore, studying the energy consumption problem in the shipbuilding process and reducing manufacturing costs are of great practical significance for the green development and low-carbon manufacturing of my country's manufacturing industry. Stamping processing is of great significance in sheet metal processing, especially in the shipbuilding process, about 65% of the sheets are stamped, which is used for the manufacture of ship sections. Compared with other mechanical processing methods, stamping processing has high production efficiency and is easy to operate, and the processed products have the characteristics of light weight and high strength.

[0003] However, during the stamping process, due to different energy consumption under different stamping process parameters, the carbon emissions generated by stamping deformation will also be different. At the same time, the stamping process parameters also affect the quality of product forming. Therefore, finding a way to reduce carbon emissions while ensuring the quality of the stamping process is an urgent problem to be solved in the current work of optimizing stamping process parameters. Summary of the invention

[0004] In view of this, it is necessary to provide a stamping process parameter optimization method, device, electronic equipment and medium to solve the problem of high carbon emissions in the prior art.

[0005] In order to solve the above problems, the present application provides a method for optimizing stamping process parameters, comprising: Obtain a data set consisting of sheet metal forming quality parameters under different stamping process parameters and total carbon emissions data during stamping processing; Fitting the data set to obtain an approximate model for predicting forming quality parameters and total carbon emissions based on stamping process parameters; Based on the approximate model and multi-objective optimization algorithm, a multi-objective optimization is performed with the goals of optimizing forming quality parameters and minimizing total carbon emissions to obtain an optimal set of process parameters.

[0006] In some possible implementations, the stamping process parameters include stamping speed and friction coefficient.

[0007] In some possible implementations, the sheet metal forming quality parameters include the maximum springback and residual stress of the sheet metal.

[0008] In some possible implementations, a data set consisting of sheet metal forming quality parameters under different stamping process parameters and total carbon emissions data during the stamping process is obtained, including: Based on the sheet metal stamping simulation model, obtain the sheet metal forming quality parameter data under different stamping process parameters; Calculate and obtain total carbon emission data during the stamping process under the stamping process parameters, wherein the total carbon emission includes carbon emission from electricity, carbon emission from raw materials, carbon emission from auxiliary materials, and carbon emission from waste; A data set is composed based on the sheet metal forming quality parameter data under the different stamping process parameters and the total carbon emissions during the stamping process.

[0009] In some possible implementations, the total carbon emissions are:

[0010]

[0011]

[0012]

[0013]

[0014] In the formula, represents the total carbon emissions, represents the carbon emissions of electricity, Indicates the power of the stamping equipment, Indicates the equipment running time. represents the carbon emission factor of the power grid, represents the carbon emissions of raw materials, Indicates the amount of raw materials used. represents the carbon emission factor of the raw materials, Indicates the carbon emissions of auxiliary materials, Indicates the amount of auxiliary materials used. represents the carbon emission factor of auxiliary materials, represents waste carbon emissions, Indicates the mass of the waste, Represents the carbon emission factor for waste treatment.

[0015] In some possible implementations, fitting the data set to obtain an approximate model for predicting forming quality parameters and total carbon emissions according to stamping process parameters includes: The data set is fitted based on the response surface method to obtain an approximate model for predicting forming quality parameters and total carbon emissions according to stamping process parameters.

[0016] In some possible implementations, based on the approximate model and the multi-objective optimization algorithm, a multi-objective optimization is performed with the goal of optimizing the forming quality parameters and minimizing the total carbon emissions to obtain an optimal process parameter set, including: S301, initializing the population using process parameters as genes; S302, based on the approximate model, calculating the index values ​​of individuals in the population, and calculating the average vector and rotation matrix of the population; S303, based on the average vector and the rotation matrix, transforming individuals in the population to a new variable space; S304, in the new variable space, based on the indicator value, using individuals in the population as parent individuals to perform a simulated binary crossover operation to generate offspring; S305, based on the average vector and the rotation matrix, transforming the offspring back to the original variable space, forming a new population based on the parent generation and offspring in the original variable space, and implementing iteration of the population; S306, return to step S302, until the number of iterations of the population reaches a preset value, and a Pareto solution set is obtained.

[0017] The present application also provides a stamping process parameter optimization device, comprising: A data acquisition unit, used to acquire a data set consisting of sheet metal forming quality parameters under different stamping process parameters and total carbon emission data during the stamping process; A data fitting unit, used for fitting the data set to obtain an approximate model for predicting forming quality parameters and total carbon emissions according to stamping process parameters; The multi-objective optimization unit is used to perform multi-objective optimization based on the approximate model and the multi-objective optimization algorithm, with the objectives of optimizing the forming quality parameters and minimizing the total carbon emissions, to obtain an optimal set of process parameters.

[0018] The present application also provides an electronic device, including a memory and a processor; The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps of the stamping process parameter optimization method described above.

[0019] The present application also provides a computer-readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the above-mentioned stamping process parameter optimization method are implemented.

[0020] The beneficial effect of the present application is that the stamping process parameter optimization method provided in the present application first obtains an approximate model by calculating and fitting the sheet metal forming quality parameters and the total carbon emission data in the stamping process under different stamping process parameters, and then uses the approximate model to perform multi-objective optimization with the goals of optimizing the forming quality parameters and minimizing the total carbon emissions, and obtains the optimal stamping process parameter combination. On the premise of meeting the stamping process forming quality, the high carbon emission problem in the low-carbon steel stamping process is effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic flow chart of an embodiment of a method for optimizing stamping process parameters provided in the present application; Figure 2 For this application Figure 1 A schematic flow chart of an embodiment of step S101; Figure 3 For this application Figure 1 A schematic flow chart of an embodiment of step S103; Figure 4 Schematic diagram of the Pareto solution set provided for this application; Figure 5 A schematic diagram of the structure of an embodiment of a stamping process parameter optimization device provided in the present application; Figure 6 A schematic diagram of the structure of an embodiment of an electronic device provided in this application. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0023] It should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowcharts may be implemented out of order, and steps without logical contextual relationships may be reversed in order or implemented simultaneously. In addition, those skilled in the art may add one or more other operations to the flowcharts, or may remove one or more operations from the flowcharts, under the guidance of the content of this application. Some of the block diagrams shown in the accompanying drawings are functional entities, and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor systems and / or microcontroller systems.

[0024] The descriptions of "first", "second", etc. involved in the embodiments of the present application are only used to describe the implicit purpose, and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the technical features defined as "first" and "second" may explicitly or implicitly include at least one of the features. "And / or" describes the association relationship of the associated objects, indicating that three relationships may exist, for example: A and / or B, which can represent: A exists alone, A and B exist at the same time, and B exists alone.

[0025] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0026] The present application provides a method for optimizing stamping process parameters, which are described below.

[0027] Figure 1 A schematic flow chart of an embodiment of the method for optimizing stamping process parameters provided in this application is shown in FIG. Figure 1 As shown in FIG. 1 , the stamping process parameter optimization method includes: S101, obtaining a data set consisting of sheet metal forming quality parameters under different stamping process parameters and total carbon emission data during the stamping process; S102, fitting the data set to obtain an approximate model for predicting forming quality parameters and total carbon emissions based on stamping process parameters; S103. Based on the approximate model and the multi-objective optimization algorithm, a multi-objective optimization is performed with the goal of optimizing the forming quality parameters and minimizing the total carbon emissions to obtain the optimal process parameter set.

[0028] Compared with the prior art, the present application first obtains an approximate model by calculating and fitting the sheet metal forming quality parameters under different stamping process parameters and the total carbon emission data during the stamping process. Then, through the approximate model, multi-objective optimization is performed with the goals of optimizing the forming quality parameters and minimizing the total carbon emissions to obtain the optimal stamping process parameter combination. Under the premise of meeting the forming quality of the stamping process, the high carbon emission problem of the low-carbon steel stamping process is effectively improved.

[0029] In order to obtain a better data set, in some embodiments, such as Figure 2 As shown, step S101 includes: S201, based on the sheet metal stamping simulation model, obtaining sheet metal forming quality parameter data under different stamping process parameters; It should be noted that in this application, the stamping process parameters are selected as stamping speed and friction coefficient, and the sheet forming quality parameters are selected as the maximum springback and residual stress of the sheet. First, the different combinations of stamping process parameters are processed by orthogonal design test method to obtain multiple numerical simulation process schemes of the stamping process, and then the finite element simulation software is used to perform numerical simulation on multiple numerical simulation process schemes to obtain the corresponding numerical simulation values ​​of residual stress and springback under each set of stamping process conditions. Among them, the unit type is shell unit, and the shell unit selects BT unit No. 2. The stamping forming simulation selects 5 integral points along the thickness direction. The maximum loading pressure is set to 200kN, the lower die does not move, the tool control uses static control, and the duration is based on the distance of the punch displacement.

[0030] S202, calculating and obtaining total carbon emission data during the stamping process under the stamping process parameters, where the total carbon emission includes carbon emission from electricity, carbon emission from raw materials, carbon emission from auxiliary materials, and carbon emission from waste; It should be noted that the total carbon emissions comprehensively consider the energy, resources and waste involved in the stamping process, analyze the factors affecting carbon emissions in the process, and define the carbon emission boundary of the stamping system to divide carbon emissions into electricity carbon emissions, raw material carbon emissions, auxiliary material carbon emissions and waste carbon emissions. Specifically, in this application, the total carbon emissions are:

[0031]

[0032]

[0033]

[0034]

[0035] In the formula, represents the total carbon emissions, represents the carbon emissions of electricity, Indicates the power of the stamping equipment, Indicates the equipment running time. represents the carbon emission factor of the power grid, represents the carbon emissions of raw materials, Indicates the amount of raw materials used. represents the carbon emission factor of the raw materials, Indicates the carbon emissions of auxiliary materials, Indicates the amount of auxiliary materials used. represents the carbon emission factor of auxiliary materials, represents waste carbon emissions, Indicates the mass of the waste, Represents the carbon emission factor for waste treatment.

[0036] S203, a data set is composed of sheet metal forming quality parameter data under different stamping process parameters and total carbon emissions during the stamping process.

[0037] In a specific embodiment, Q235 low carbon steel stamping is used as an example for explanation. A spherical plate with a size of 1000mm×1000mm×10mm and a forming radius of 1900mm×1900mm is used as the processing object, and a three-dimensional model of multi-point forming is established; a simulation model is established based on the material performance parameters and stamping process parameters of the Q235 curved curved plate, and the stamping process of the Q235 curved curved plate is simulated according to different stamping process parameters; the processing of the spherical plate is divided into three steps. First, the lower die is adjusted to the target shape, and the sheet to be formed is placed in it. Then the basic body of the upper die moves downward in sequence, and finally the upper and lower basic bodies are pressed against each other to complete the processing of the sheet. After the simulation, the maximum springback and residual stress of the curved plate are obtained.

[0038] During the stamping process of low-carbon steel, the process parameters that affect the carbon emission and forming quality of the steel plate include stamping speed and friction coefficient. In order to explore the influence of stamping process parameters on total carbon emission, residual stress and springback, the stamping process was optimized and the stamping test was designed using the orthogonal experimental method. The levels of each factor in the test are shown in Table 1: Table 1: Orthogonal test factors and their level values

[0039] In Table 1, V represents the punching speed, and M represents the friction coefficient.

[0040] A 16-group design with 2 factors and 4 levels was adopted. The experimental scheme and calculation results are shown in Table 2: Table 2: Stamping test plan and experimental results

[0041] In Table 2, V represents the punching speed, M represents the friction coefficient, C represents the total carbon emission, F represents the residual stress, and H represents the springback.

[0042] In order to obtain a better approximate model, in some embodiments, step S102 uses the response surface method to perform data fitting, which specifically includes: 1) Model fitting: The collected data are fitted into an approximate model. In this application, a second-order model is used to describe the relationship between the response variable and one or more independent variables. This model can help predict the response at any point in the variable space. The independent variables in the second-order model are various influencing factors. The goal is to find the level value of each influencing factor to optimize the response. If this point exists, it should make the function's partial derivatives zero. The optimal point is generally a pole, which is a concave or convex point or a saddle point on a surface.

[0043] 2) Significance and accuracy analysis: The significant effect of each factor and its interaction on the response is determined by analysis of variance (ANOVA) (calculate the P value of each factor. Generally, if the P value is less than 0.05, it means that the factor has a significant effect on the response). The accuracy of the model is determined by calculating evaluation indicators such as the coefficient of determination (R²).

[0044] 3) Model evaluation and adjustment: Conduct regression experiments under optimized conditions to verify whether the results predicted by the model can be achieved. According to the results of model evaluation, modify the model, such as reselecting variables, adding or reducing interaction terms and quadratic terms, etc.

[0045] Based on the above method, in a specific embodiment, the test results in Table 2 are simulated, modeled, and compared, and the best mathematical model is selected to obtain an approximate model:

[0046]

[0047]

[0048] Where V represents the punching speed, M represents the friction coefficient, C represents the total carbon emission, F represents the residual stress, and H represents the springback.

[0049] In order to better perform multi-objective optimization, in some embodiments, an improved IBEA algorithm is used. Specifically, Figure 3 As shown, step S103 includes: S301, initializing the population using process parameters as genes; S302, based on the approximate model, calculating the index values ​​of individuals in the population, and calculating the average vector and rotation matrix of the population; It should be noted that the average vector is calculated first , and then calculate the rotation matrix by the average vector , and finally the covariance matrix Perform eigenvalue decomposition: , where represents the rotation matrix, represents a diagonal matrix containing the eigenvalues ​​of the covariance matrix.

[0050] S303, based on the average vector and the rotation matrix, transforming the individuals in the population to a new variable space; It should be noted that the formula Will Transformed to .

[0051] S304, in the new variable space, based on the index value, using individuals in the population as parent individuals to perform a simulated binary crossover operation to generate offspring; S305, based on the average vector and the rotation matrix, the offspring is transformed back to the original variable space, and a new population is formed based on the parent generation and the offspring in the original variable space to implement iteration of the population; S306, return to step S302, until the number of iterations of the population reaches a preset value, and a Pareto solution set is obtained.

[0052] In a specific embodiment, the parameters of the improved IBEA algorithm are set as follows: the population size is 100, the maximum number of iterations is 500, the crossover probability is 0.9, the mutation probability is 0.01, and the target vector size is 3; with low total carbon emissions, low springback, and low residual stress as the goals, the Pareto solution set is obtained by using matlab programming. Figure 4 shown.

[0053] After analyzing the Pareto optimal solution set, the recommended solutions in Table 3 are given: Table 3: Recommended solutions

[0054] If the main optimization target is total carbon emissions, it is recommended to adopt Option 1 to minimize carbon emissions; if springback is the main optimization target, Option 2 should be selected to obtain the lowest springback; if residual stress is the main optimization target, Option 4 should be considered to reduce residual stress; and if the forming energy consumption and forming quality are to be considered comprehensively, Option 1 is recommended, because in this case, the values ​​of carbon emissions and forming quality are at a relatively optimal level, which can reduce carbon emissions while ensuring forming quality.

[0055] In order to further determine the quality of the optimization effect, the empirical value is compared with the optimized value, as shown in Table 4: Table 4 Comparison of stamping process before and after optimization

[0056] It can be seen from Table 4 that the effect of the optimized value is significantly better than the empirical value; after optimization, the total carbon emissions are reduced from 218.475kg to 212.041kg, a decrease of 2.945%; the springback is reduced from 8.325mm to 8.003mm, a decrease of 3.868%; the residual stress is reduced from 255.238MPa to 220.557MPa, a decrease of 13.588%; the above data show that after optimization, the product quality is improved and the carbon emissions generated during processing are effectively reduced.

[0057] In order to better implement a stamping process parameter optimization method in an embodiment of the present application, based on a stamping process parameter optimization method, correspondingly, Figure 5 As shown, the embodiment of the present application further provides a stamping process parameter optimization device 500, comprising: A data acquisition unit 501 is used to acquire a data set consisting of sheet metal forming quality parameters under different stamping process parameters and total carbon emission data during the stamping process; A data fitting unit 502 is used to fit the data set to obtain an approximate model for predicting forming quality parameters and total carbon emissions according to stamping process parameters; The multi-objective optimization unit 503 is used to perform multi-objective optimization based on the approximate model and the multi-objective optimization algorithm, with the objectives of optimizing the forming quality parameters and minimizing the total carbon emissions, to obtain the optimal process parameter set.

[0058] The stamping process parameter optimization device 500 provided in the above embodiment can implement the technical solution described in the above stamping process parameter optimization method embodiment. The specific implementation principles of the above units can refer to the corresponding contents in the above stamping process parameter optimization method embodiment, which will not be repeated here.

[0059] like Figure 6 As shown, the present application also provides an electronic device 600. The electronic device 600 includes a processor 601, a memory 602 and a display 603. Figure 6 Only some components of the electronic device 600 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0060] In some embodiments, the memory 602 may be an internal storage unit of the electronic device 600, such as a hard disk or memory of the electronic device 600. In other embodiments, the memory 602 may also be an external storage device of the electronic device 600, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 600.

[0061] Furthermore, the memory 602 may include both an internal storage unit of the electronic device 600 and an external storage device. The memory 602 is used to store application software installed in the electronic device 600 and various data.

[0062] In some embodiments, the processor 601 may be a central processing unit (CPU), a microprocessor or other data processing chip, which is used to run program codes or process data stored in the memory 602, such as the stamping process parameter optimization method in the present application.

[0063] In some embodiments, the display 603 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 603 is used to display information on the electronic device 600 and to display a visual user interface. The components 601-603 of the electronic device 600 communicate with each other via a system bus.

[0064] In some embodiments of the present application, when the processor 601 executes the stamping process parameter optimization program in the memory 602, the following steps may be implemented: Obtain a data set consisting of sheet metal forming quality parameters under different stamping process parameters and total carbon emissions data during stamping processing; The data set is fitted to obtain an approximate model for predicting forming quality parameters and total carbon emissions based on stamping process parameters; Based on the approximate model and multi-objective optimization algorithm, multi-objective optimization is carried out with the goals of optimizing forming quality parameters and minimizing total carbon emissions to obtain the optimal set of process parameters.

[0065] It should be understood that: when the processor 601 executes the stamping process parameter optimization program in the memory 602, in addition to the above functions, other functions can also be implemented. For details, please refer to the description of the corresponding method embodiment above.

[0066] Furthermore, the embodiment of the present application does not specifically limit the type of the electronic device 600 mentioned, and the electronic device 600 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present application, the electronic device 600 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0067] Accordingly, an embodiment of the present application also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, the steps or functions of the stamping process parameter optimization method provided in the above-mentioned method embodiments can be implemented.

[0068] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the above-mentioned program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0069] The above is a detailed introduction to a stamping process parameter optimization method provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, based on the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0070] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed in the present application should be covered within the protection scope of the present application.

Claims

1. A method for optimizing stamping process parameters, characterized in that: include: Obtain a data set consisting of sheet metal forming quality parameters under different stamping process parameters and total carbon emissions data during stamping processing; Fitting the data set to obtain an approximate model for predicting forming quality parameters and total carbon emissions based on stamping process parameters; Based on the approximate model and multi-objective optimization algorithm, a multi-objective optimization is performed with the goals of optimizing forming quality parameters and minimizing total carbon emissions to obtain an optimal set of process parameters.

2. The method for optimizing stamping process parameters according to claim 1, characterized in that: The stamping process parameters include stamping speed and friction coefficient.

3. The method for optimizing stamping process parameters according to claim 1, characterized in that: The sheet metal forming quality parameters include the maximum springback and residual stress of the sheet metal.

4. The method for optimizing stamping process parameters according to claim 1, characterized in that: Obtain a data set consisting of sheet metal forming quality parameters under different stamping process parameters and total carbon emissions data during the stamping process, including: Based on the sheet metal stamping simulation model, obtain the sheet metal forming quality parameter data under different stamping process parameters; Calculate and obtain total carbon emission data during the stamping process under the stamping process parameters, wherein the total carbon emission includes carbon emission from electricity, carbon emission from raw materials, carbon emission from auxiliary materials, and carbon emission from waste; A data set is composed based on the sheet metal forming quality parameter data under the different stamping process parameters and the total carbon emissions during the stamping process.

5. The method for optimizing stamping process parameters according to claim 4, characterized in that: The total carbon emissions are: In the formula, represents the total carbon emissions, represents the carbon emissions of electricity, Indicates the power of the stamping equipment, Indicates the equipment running time. represents the carbon emission factor of the power grid, represents the carbon emissions of raw materials, Indicates the amount of raw materials used. represents the carbon emission factor of the raw material, Indicates the carbon emissions of auxiliary materials, Indicates the amount of auxiliary materials used. represents the carbon emission factor of auxiliary materials, represents waste carbon emissions, Indicates the mass of the waste, Represents the carbon emission factor for waste treatment.

6. The method for optimizing stamping process parameters according to claim 1, characterized in that: The data set is fitted to obtain an approximate model for predicting forming quality parameters and total carbon emissions according to stamping process parameters, including: The data set is fitted based on the response surface method to obtain an approximate model for predicting forming quality parameters and total carbon emissions according to stamping process parameters.

7. The method for optimizing stamping process parameters according to claim 1, characterized in that: Based on the approximate model and multi-objective optimization algorithm, multi-objective optimization is performed with the goal of optimizing the forming quality parameters and minimizing the total carbon emissions, and the optimal process parameter set is obtained, including: S301, initializing the population using process parameters as genes; S302, based on the approximate model, calculating the index values ​​of individuals in the population, and calculating the average vector and rotation matrix of the population; S303, based on the average vector and the rotation matrix, transforming individuals in the population to a new variable space; S304, in the new variable space, based on the indicator value, using individuals in the population as parent individuals to perform a simulated binary crossover operation to generate offspring; S305, based on the average vector and the rotation matrix, transforming the offspring back to the original variable space, forming a new population based on the parent generation and offspring in the original variable space, and implementing iteration of the population; S306, return to step S302, until the number of iterations of the population reaches a preset value, and a Pareto solution set is obtained.

8. A stamping process parameter optimization device, characterized in that: include: A data acquisition unit, used to acquire a data set consisting of sheet metal forming quality parameters under different stamping process parameters and total carbon emission data during the stamping process; A data fitting unit, used for fitting the data set to obtain an approximate model for predicting forming quality parameters and total carbon emissions according to stamping process parameters; The multi-objective optimization unit is used to perform multi-objective optimization based on the approximate model and the multi-objective optimization algorithm, with the objectives of optimizing the forming quality parameters and minimizing the total carbon emissions, to obtain an optimal set of process parameters.

9. An electronic device, characterized in that: including memory and processor; The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps of the stamping process parameter optimization method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The readable storage medium stores a program or instruction, and when the program or instruction is executed by a processor, the steps of the stamping process parameter optimization method described in any one of claims 1 to 7 are implemented.

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