Metal plate serialization parameterization design method, system, equipment and medium

Abstract

The invention relates to a metal plate series parameterization design method, system and device and a medium, and the method comprises the steps: obtaining the design information of a target metal plate part, analyzing the design information of the target metal plate part, and obtaining the design demands and target design parameters of the target metal plate part; performing feature extraction on the design demand of the target sheet metal part to obtain demand feature point data; inputting the demand feature point data into a pre-trained design model for analysis to obtain a design parameter template of the initial sheet metal part; performing three-dimensional modeling according to the design parameter template of the initial sheet metal part and the target design parameters to obtain a three-dimensional model of the initial sheet metal part; importing the three-dimensional model of the initial sheet metal part into a virtual reality environment for interactive simulation and evaluation to obtain an evaluation result; and adjusting the three-dimensional model of the initial sheet metal part according to an evaluation result to obtain a design graph of the target sheet metal part. The method has the effect of improving the metal plate design efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of sheet metal processing, and in particular, to a design method, system, device and medium for sheet metal serialization and parameterization. Background Art

[0002] Currently, in the sheet metal processing industry, as the manufacturing industry has higher and higher requirements for efficiency and cost control, the traditional method of manually drawing drawings and then converting them into numerical control programming can no longer meet the needs of modern industry. And the automated design based on CAD / CAM software has become one of the mainstream trends, which improves the design efficiency and at the same time ensures the processing accuracy. However, most of the existing designs still stay at the single part level and lack support for serialization and parameterization. The common practice in the industry currently is to use general CAD software for 3D modeling, and then generate NC codes through a CAM system to guide the production equipment to complete the processing. Although this method can better adapt to the design requirements of products with different shapes, when facing a large number of standardized or similar-structured sheet metal parts, there is a lot of repetitive labor, low efficiency, and it is not easy to maintain and update.

[0003] The above-mentioned existing technical solutions have the following defects: The traditional sheet metal design method has problems such as long design cycle, high error rate, and difficulty in realizing batch customization. Especially when facing a series of sheet metal parts with slightly different sizes but the same basic structure, a lot of repetitive labor is required, resulting in low design efficiency, so there is room for improvement. Summary of the Invention

[0004] In order to improve the efficiency of sheet metal design, the present application provides a design method, system, device and medium for sheet metal serialization and parameterization.

[0005] The first invention object of the present application is achieved through the following technical solutions: A design method for sheet metal serialization and parameterization, the design method for sheet metal serialization and parameterization includes: Obtain the design information of the target sheet metal part, analyze the design information of the target sheet metal part based on Bayes' theorem, and obtain the design requirements and target design parameters of the target sheet metal part; Use a feature extraction algorithm to extract features from the design requirements of the target sheet metal part to obtain demand feature point data; Input the demand feature point data into a pre-trained design model for analysis to obtain a design parameter template of the initial sheet metal part; Perform 3D modeling according to the design parameter template of the initial sheet metal part and the target design parameters to obtain a 3D model of the initial sheet metal part; Import the 3D model of the initial sheet metal part into a virtual reality environment for interactive simulation and evaluation to obtain an evaluation result; Adjust the 3D model of the initial sheet metal part according to the evaluation result to obtain the design drawing of the target sheet metal part, and export the design drawing of the target sheet metal part to the user terminal.

[0006] By adopting the above technical solutions, by collecting the detailed design information of the target sheet metal part, using Bayes' theorem to analyze the collected detailed design information of the target sheet metal part, and combining prior knowledge and observed data to infer the posterior probability distribution of the design parameters of the target sheet metal part, more accurate design requirements and parameter estimates can be obtained, which helps to ensure that the final product meets the expected functional and quality standards; through the feature extraction algorithm, the key feature point data describing the design requirements of the sheet metal part can be obtained. Feature extraction can extract key features from a large amount of complex data, reduce the data dimension, facilitate subsequent processing and analysis. Through feature extraction, potential patterns and associations in the design requirements can be identified, providing valuable opinions for optimizing the design; by using the extracted feature point data as input and providing it to a pre-trained design model, the model can provide high-quality design parameters based on a large amount of historical data and optimization algorithms, improving the performance and quality of the final product; by performing 3D modeling according to the design parameter template of the initial sheet metal part and the target design parameters, the 3D model of the initial sheet metal part is obtained. 3D modeling transforms abstract design parameters into specific visual models, facilitating designers to understand and evaluate the design scheme, and providing a basis for subsequent design adjustment and optimization, supporting rapid iteration and improvement; by importing the 3D model of the initial sheet metal part into a virtual reality environment for interactive simulation and evaluation, the usage experience of the end user can be better understood, and the design can be optimized to meet the user's needs; by adjusting the 3D model of the initial sheet metal part according to the evaluation result, it helps to ensure that the design of the sheet metal part meets the functional requirements, performance standards and manufacturing constraints, improving the accuracy and reliability of the design. Export the adjusted design drawing of the target sheet metal part from the virtual reality environment or 3D modeling software for communication and confirmation with customers or relevant parties to ensure the consistency of the design objectives.

[0007] In a preferred example of the present application, it can be further configured that: the obtaining of the design information of the target sheet metal part and the analysis of the design information of the target sheet metal part based on Bayes' theorem to obtain the design requirements and target design parameters of the target sheet metal part include: Collect the design information of the target sheet metal part input by the user through the user interface, and the design information of the target sheet metal part includes part dimensions, material type, processing technology requirements and functional requirements; Apply a Bayesian classifier to perform probability statistical analysis on the design information of the target sheet metal part, identify the key parameters and potential requirements in the design, and obtain the statistical analysis result; Generate the design requirements and the target design parameters of the target sheet metal part according to the statistical analysis results.

[0008] By adopting the above technical solution, by collecting the design information of the target sheet metal part input by the user through the user interface, it helps to comprehensively understand the specific needs and design requirements of the user, so as to ensure that the subsequent design process can accurately match the user's expectations. By collecting in detail the part dimensions, material types, processing technology requirements and functional requirements, it can provide a solid foundation for subsequent data analysis and design optimization, improving the accuracy and reliability of the design; by applying the Bayesian classifier to conduct probabilistic statistical analysis on the design information, it can effectively identify the key parameters and potential requirements in the design, thus helping the design team focus on the most important design elements and optimize the design process; by generating design requirements and design parameters according to the statistical analysis results, it can ensure that the design scheme is based on reliable data analysis, improving the accuracy and adaptability of the design, and by generating clear design requirements and parameters, it can provide clear guidance for the design team, reducing the uncertainty and repetition in the design process, and improving the overall design efficiency and product quality.

[0009] In a preferred example of the present application, it can be further configured as follows: The feature extraction algorithm is used to extract features from the design requirements of the target sheet metal part, and the obtained requirement feature point data includes: Apply an edge detection algorithm to identify the geometric contour and key structure in the design requirements of the target sheet metal part; Adopt shape matching technology to extract the standardized features in the design requirements of the target sheet metal part; Normalize the geometric contour, the key structure and the standardized features to obtain the requirement feature point data.

[0010] By adopting the above technical solution, by applying an edge detection algorithm to identify the geometric contour and key structure of the target sheet metal part, it can accurately capture the details and shape features in the design requirements, thus ensuring the geometric accuracy of the design model; by adopting shape matching technology to extract the standardized features in the design requirements, it can achieve rapid comparison and matching between different designs, improving the efficiency and consistency of the design process; by normalizing the geometric contour, key structure and standardized features, it can eliminate the dimensional differences between different data sources, enabling the feature data to be analyzed and processed on a unified scale, improving the accuracy of data processing.

[0011] In a preferred example of the present application, it can be further configured as follows: Before inputting the requirement feature point data into a pre-trained design model for analysis to obtain the design parameter template of the initial sheet metal part, the above-mentioned design method for sheet metal serialization and parameterization further includes: Obtain the data of various types of standard sheet metal parts, preprocess and label the data of the various types of standard sheet metal parts to obtain a training set; Use the training set to perform forward propagation and backpropagation training on a design model constructed based on a deep learning neural network to obtain a design model after forward propagation and backpropagation training; Use a genetic algorithm to optimize the parameters of the design model after forward propagation and backpropagation training to obtain the pre-trained design model.

[0012] By adopting the above technical solutions, by obtaining the data of various types of standard sheet metal parts and performing preprocessing and labeling, a diverse and high-quality training set can be constructed to ensure that the neural network can learn rich features and patterns during the training process, improving the generalization ability of the model; by using the training set to perform forward propagation and backpropagation training on a deep learning neural network, the weights and parameters of the model can be effectively adjusted to enable it to accurately capture the complex relationship between design requirements and design parameters, improving the prediction accuracy of the model; by using a genetic algorithm to optimize the parameters of the trained design model, the optimal parameter combination can be searched globally to avoid falling into local optimal solutions, thereby further improving the performance and accuracy of the model.

[0013] In a preferred example of the present application, it can be further configured that: the inputting the demand feature point data into the pre-trained design model for analysis to obtain the design parameter template of the initial sheet metal part includes: Receive the demand feature point data, and perform preliminary data preprocessing on the demand feature point data to obtain preprocessed feature point data; Perform deep feature extraction and pattern recognition on the preprocessed feature point data through a multi-layer neural network to obtain the parameter pattern of the feature points; Analyze and design the parameter pattern of the feature points to obtain the design parameter template of the initial sheet metal part.

[0014] By adopting the above technical solutions, by receiving the demand feature point data and performing preliminary data preprocessing, the original data can be cleaned and normalized, and noise and outliers can be removed, thereby improving the quality and consistency of the data; by performing deep feature extraction through a multi-layer neural network, high-level features in the data can be automatically learned and extracted, complex patterns and relationships can be captured, and the expression ability of the model can be enhanced. Through pattern recognition, the parameter patterns of feature points can be recognized and classified, providing an accurate basis for the subsequent generation of design parameter templates, and improving the intelligence and automation level of the design process; by analyzing and designing the parameter patterns of feature points, the extracted patterns can be transformed into specific design parameter templates to ensure the standardization and consistency of the design process.

[0015] In a preferred example, the present application can be further configured as follows: The three-dimensional modeling according to the design parameter template of the initial sheet metal part and the target design parameters to obtain the three-dimensional model of the initial sheet metal part includes: Based on the CAD software platform, import the design parameter template of the initial sheet metal part to generate a preliminary three-dimensional model; Apply parametric modeling technology to dynamically adjust the geometric shape and dimensions of the preliminary three-dimensional model based on the target design parameters, and integrate finite element analysis to evaluate the structural strength and process feasibility of the three-dimensional model, optimize the design parameters, and finally obtain the three-dimensional model of the initial sheet metal part.

[0016] By adopting the above technical solution, by importing the initial design parameter template based on the CAD software platform, the two-dimensional design parameters can be quickly converted into a three-dimensional model, significantly shortening the design cycle. The generated preliminary three-dimensional model provides an intuitive visual effect, which helps designers better understand and evaluate the design scheme and discover potential problems in advance; by applying parametric modeling technology, the geometric shape and dimensions of the three-dimensional model can be dynamically adjusted according to the target design parameters, realizing the flexibility and adjustability of the design, meeting different design requirements, and integrating finite element analysis (FEA) to evaluate the structural strength and process feasibility of the three-dimensional model, which can identify the structural weaknesses and manufacturing difficulties in the design in advance and reduce the cost of later modification and rework.

[0017] In a preferred example, the present application can be further configured as follows: The method for parametric design of a sheet metal series further includes: Record the log information of each design modification, and optimize and adjust the pre-trained design model according to the log information; Obtain the operation habits and preference data of the designer, analyze the operation habits and preference data of the designer by using big data analysis technology to obtain the preference analysis result, and dynamically adjust the interface layout and function modules based on the preference analysis result to obtain a customized design environment.

[0018] By adopting the above technical solutions, by recording the log information of each design modification and optimizing and adjusting the pre-trained design model according to the log information, every change in the design process can be comprehensively tracked, ensuring the transparency and traceability of the design process, and continuously improving the accuracy and adaptability of the model to ensure that the model always reflects the latest design requirements and practical experience; by obtaining the operation habits and preference data of designers, using big data analysis technology to analyze the operation habits and preference data, and dynamically adjusting the interface layout and function modules based on the preference analysis results to obtain a customized design environment, the working methods and preferences of designers can be deeply understood, the user-friendliness of the design tool can be improved, the adaptability and competitiveness of the design tool can be enhanced, the diverse needs of different designers can be met, and the user base can be expanded.

[0019] The second inventive object of the present application is achieved by the following technical solutions: A sheet metal series and parameterized design system, the sheet metal series and parameterized design system includes: A requirement acquisition module, configured to acquire the design information of the target sheet metal part, analyze the design information of the target sheet metal part based on Bayes' theorem, and obtain the design requirements and target design parameters of the target sheet metal part; A feature extraction module, configured to extract features from the design requirements of the target sheet metal part by using a feature extraction algorithm to obtain requirement feature point data; A model analysis module, configured to input the requirement feature point data into a pre-trained design model for analysis to obtain a design parameter template of the initial sheet metal part; A three-dimensional construction module, configured to perform three-dimensional modeling according to the design parameter template of the initial sheet metal part and the target design parameters to obtain a three-dimensional model of the initial sheet metal part; A simulation evaluation module, configured to import the three-dimensional model of the initial sheet metal part into a virtual reality environment for interactive simulation and evaluation to obtain an evaluation result; A generation and export module, configured to adjust the three-dimensional model of the initial sheet metal part according to the evaluation result to obtain the design drawing of the target sheet metal part, and export the design drawing of the target sheet metal part to the user side.

[0020] By adopting the above technical solutions, by collecting the detailed design information of the target sheet metal part, using Bayes' theorem to analyze the collected detailed design information of the target sheet metal part, and combining prior knowledge and observed data to infer the posterior probability distribution of the design parameters of the target sheet metal part, more accurate design requirements and parameter estimates can be obtained, which helps to ensure that the final product meets the expected functional and quality standards; through the feature extraction algorithm, the key feature point data describing the design requirements of the sheet metal part can be obtained. Feature extraction can extract key features from a large amount of complex data, reduce the data dimension, and facilitate subsequent processing and analysis. Through feature extraction, potential patterns and associations in the design requirements can be identified, providing valuable opinions for optimizing the design; by using the extracted feature point data as input and providing it to a pre-trained design model, the model can provide high-quality design parameters based on a large amount of historical data and optimization algorithms, improving the performance and quality of the final product; by performing 3D modeling according to the design parameter template of the initial sheet metal part and the target design parameters, a 3D model of the initial sheet metal part is obtained. 3D modeling transforms abstract design parameters into specific visual models, facilitating designers to understand and evaluate the design scheme, and providing a basis for subsequent design adjustment and optimization, supporting rapid iteration and improvement; by importing the 3D model of the initial sheet metal part into a virtual reality environment for interactive simulation and evaluation, a better understanding of the end-user's usage experience can be obtained, and the design can be optimized to meet user needs; by adjusting the 3D model of the initial sheet metal part according to the evaluation results, it helps to ensure that the design of the sheet metal part meets the functional requirements, performance standards, and manufacturing constraints, improving the accuracy and reliability of the design. Export the adjusted design graphics of the target sheet metal part from the virtual reality environment or 3D modeling software for easy communication and confirmation with customers or relevant parties to ensure the consistency of design goals.

[0021] The above object three of the present application is achieved by the following technical solutions: A computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above design method for sheet metal serialization and parameterization are implemented.

[0022] The above object four of the present application is achieved by the following technical solutions: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above design method for sheet metal serialization and parameterization are implemented.

[0023] In summary, the present application includes at least one of the following beneficial technical effects: 1. By collecting the detailed design information of the target sheet metal part, using Bayes' theorem to analyze the collected detailed design information of the target sheet metal part, and combining prior knowledge and observed data to infer the posterior probability distribution of the design parameters of the target sheet metal part, more accurate design requirements and parameter estimates can be obtained, which helps to ensure that the final product meets the expected functional and quality standards; through the feature extraction algorithm, key feature point data describing the design requirements of the sheet metal part can be obtained. Feature extraction can extract key features from a large amount of complex data, reduce the data dimension, facilitate subsequent processing and analysis, and through feature extraction, potential patterns and associations in the design requirements can be identified, providing valuable opinions for optimizing the design; by using the extracted feature point data as input and providing it to a pre-trained design model, the model can provide high-quality design parameters based on a large amount of historical data and optimization algorithms, improving the performance and quality of the final product. 2. By performing 3D modeling based on the design parameter template of the initial sheet metal part and the target design parameters, a 3D model of the initial sheet metal part is obtained. 3D modeling converts abstract design parameters into specific visual models, facilitating designers to understand and evaluate the design scheme, and providing a basis for subsequent design adjustment and optimization, supporting rapid iteration and improvement; by importing the 3D model of the initial sheet metal part into a virtual reality environment for interactive simulation and evaluation, the usage experience of the end user can be better understood, and the design can be optimized to meet user requirements; by adjusting the 3D model of the initial sheet metal part according to the evaluation results, it helps to ensure that the design of the sheet metal part meets the functional requirements, performance standards, and manufacturing constraints, improving the accuracy and reliability of the design. Export the adjusted design drawing of the target sheet metal part from the virtual reality environment or 3D modeling software for communication and confirmation with customers or relevant parties to ensure the consistency of the design objectives. Description of the Drawings

[0024] Figure 1 is a flowchart of a design method for sheet metal serialization and parameterization in an embodiment of the present application; Figure 2 is an implementation flowchart of step S10 in a design method for sheet metal serialization and parameterization in an embodiment of the present application; Figure 3 is an implementation flowchart of step S20 in a design method for sheet metal serialization and parameterization in an embodiment of the present application; Figure 4 is an implementation flowchart of step S30 in a design method for sheet metal serialization and parameterization in an embodiment of the present application; Figure 5 is an implementation flowchart of step S30 in a design method for sheet metal serialization and parameterization in an embodiment of the present application; Figure 6It is the implementation flowchart of step S40 in a design method for sheet metal serialization and parameterization in an embodiment of the present application; Figure 7 It is an implementation flowchart of a design method for sheet metal serialization and parameterization in an embodiment of the present application; Figure 8 It is a principle block diagram of a design system for sheet metal serialization and parameterization in an embodiment of the present application; Figure 9 It is a schematic diagram of the equipment in an embodiment of the present application. Detailed implementation manners

[0025] The present application will be further described in detail below with reference to the accompanying drawings.

[0026] In one embodiment, as Figure 1 shown, the present application discloses a design method for sheet metal serialization and parameterization, which specifically includes the following steps: S10: Obtain the design information of the target sheet metal part, analyze the design information of the target sheet metal part based on Bayes' theorem, and obtain the design requirements and target design parameters of the target sheet metal part.

[0027] Specifically, collect the detailed design information of the target sheet metal part, which may include material type, thickness, dimensions, expected functions and performance requirements, etc., providing the basic data for analyzing and determining the design requirements. Apply Bayes' theorem to analyze the collected design information. Through Bayesian analysis, the posterior probability distribution of the design parameters of the target sheet metal part can be inferred by combining prior knowledge and observed data, so as to obtain more accurate design requirements and parameter estimates. According to the results of Bayesian analysis, determine the specific design requirements and target design parameters of the sheet metal part, such as performance standards and safety requirements that must be met, as well as target design parameters, such as dimensional tolerances and material properties.

[0028] S20: Use a feature extraction algorithm to extract features from the design requirements of the target sheet metal part to obtain demand feature point data.

[0029] Specifically, through feature extraction algorithms, such as statistical analysis, machine learning or deep learning methods, they can identify the most important feature point data in the design requirements, so as to obtain the key feature point data describing the design requirements of the sheet metal part, such as material properties, dimensional tolerances, surface treatment requirements, durability standards, etc. Finally, integrate the extracted data to obtain demand feature point data.

[0030] S30: Input the demand feature point data into a pre-trained design model for analysis to obtain the design parameter template of the initial sheet metal part.

[0031] Specifically, the extracted requirement feature point data is used as input and provided to a pre-trained design model, which may be based on machine learning or deep learning techniques such as convolutional neural network (CNN), long short-term memory network (LSTM), or support vector machine (SVM). The design model analyzes the input feature point data and generates an initial sheet metal part design parameter template, which contains the basic parameters and settings required for designing the sheet metal part and can be used as the starting point or reference for the design process.

[0032] S40: Perform 3D modeling based on the initial sheet metal part design parameter template and the target design parameters to obtain a 3D model of the initial sheet metal part.

[0033] Specifically, use computer-aided design (CAD) software or 3D modeling tools to perform 3D modeling based on the initial design parameter template and the target design parameters. Through the 3D modeling process, a 3D model of the sheet metal part is generated, which details the shape, size, and structure of the sheet metal part. During the 3D modeling process, designers can utilize various modeling tools and functions such as extrusion, rotation, sweeping, and Boolean operations to construct the geometry of the sheet metal part and finally adjust to obtain the 3D model of the initial sheet metal part.

[0034] S50: Import the 3D model of the initial sheet metal part into a virtual reality environment for interactive simulation and evaluation to obtain an evaluation result.

[0035] Specifically, the 3D model of the sheet metal part created using 3D modeling software such as SolidWorks or 3ds Max is imported into a virtual reality platform through a specific data format such as OBJ or FBX. In the virtual reality environment, users can interact with the sheet metal part model, such as rotating, scaling, disassembling, or simulating the assembly process, to provide an immersive experience that enables users to examine the design from different angles and levels of detail. After completing the simulation and evaluation, the system generates an evaluation report summarizing the findings, problems, and proposed improvement measures during the simulation. Through interactive simulation, potential problems in the design such as assembly interference, dimensional mismatch, or functional defects can be discovered, allowing for necessary modifications and optimizations before production.

[0036] S60: Adjust the 3D model of the initial sheet metal part according to the evaluation result to obtain the design drawing of the target sheet metal part and export the design drawing of the target sheet metal part to the user side.

[0037] Specifically, in a virtual reality environment, based on the problems found in the evaluation results and the proposed suggestions, necessary modifications and optimizations are made to the 3D model of the initial sheet metal part, such as changing dimensions, adjusting shapes, modifying material properties, or optimizing assembly relationships, etc. The adjusted 3D model now becomes the design drawing of the target sheet metal part, which is a complete and verified design that can be used for manufacturing and production. The adjusted design drawing of the target sheet metal part is exported from the virtual reality environment or 3D modeling software and is usually saved in a specific file format (such as DXF, STEP, IGES, etc.) for use in different computer-aided design (CAD) systems or manufacturing execution systems (MES).

[0038] By adopting the above technical solutions, by collecting the detailed design information of the target sheet metal part, using Bayes' theorem to analyze the collected detailed design information of the target sheet metal part, and combining prior knowledge and observed data to infer the posterior probability distribution of the design parameters of the target sheet metal part, more accurate design requirements and parameter estimates can be obtained, which helps to ensure that the final product meets the expected functional and quality standards; through the feature extraction algorithm, key feature point data describing the design requirements of the sheet metal part can be obtained. Feature extraction can extract key features from a large amount of complex data, reduce the data dimension, facilitate subsequent processing and analysis, and through feature extraction, potential patterns and associations in the design requirements can be identified, providing valuable opinions for optimizing the design; by using the extracted feature point data as input and providing it to a pre-trained design model, the model can provide high-quality design parameters based on a large amount of historical data and optimization algorithms, improving the performance and quality of the final product; by performing 3D modeling according to the design parameter template of the initial sheet metal part and the target design parameters, a 3D model of the initial sheet metal part is obtained. 3D modeling transforms abstract design parameters into specific visual models, facilitating designers to understand and evaluate the design scheme and providing a basis for subsequent design adjustment and optimization, supporting rapid iteration and improvement; by importing the 3D model of the initial sheet metal part into the virtual reality environment for interactive simulation and evaluation, the usage experience of the end user can be better understood, and the design can be optimized to meet user needs; by adjusting the 3D model of the initial sheet metal part according to the evaluation results, it helps to ensure that the design of the sheet metal part meets the functional requirements, performance standards, and manufacturing constraints, improving the accuracy and reliability of the design. The adjusted design drawing of the target sheet metal part is exported from the virtual reality environment or 3D modeling software to facilitate communication and confirmation with customers or relevant parties, ensuring the consistency of design goals.

[0039] In one embodiment, as Figure 2 shown, in step S10, that is, obtaining the design information of the target sheet metal part, analyzing the design information of the target sheet metal part based on Bayes' theorem, and obtaining the design requirements and target design parameters of the target sheet metal part, specifically including: S11: Collect the design information of the target sheet metal part input by the user through the user interface. The design information of the target sheet metal part includes part dimensions, material type, processing technology requirements, and functional requirements.

[0040] Specifically, through the user interface, designers or engineers can directly input or upload design parameters. The user inputs data through the graphical user interface (GUI), such as filling out online forms, selecting options from dropdown menus, uploading CAD files, or using other interactive tools. Statistically integrate the collected design information to obtain the design information of the target sheet metal part, which includes part dimensions, material type, processing technology requirements, and functional requirements. The collected design information of the target sheet metal part will be used to guide the detailed design process of the sheet metal part, such as 3D modeling, generation of engineering drawings, material selection, and processing technology planning.

[0041] S12: Apply the Bayesian classifier to perform probability statistical analysis on the design information of the target sheet metal part, identify the key parameters and potential requirements in the design, and obtain the statistical analysis result.

[0042] Specifically, apply the Bayesian classifier to perform probability statistical analysis on the design information of the target sheet metal part. During the analysis process, the Bayesian classifier will use statistical methods to identify the key parameters and potential requirements in the design information, such as modeling the probability distributions of different parameters and evaluating how these parameters affect the probability of the final product design. Through probability statistical analysis, the Bayesian classifier can identify which parameters are the most critical for the design of the sheet metal part and the potential requirements that may affect the success of the design. For example, certain dimension parameters may be crucial for the functionality of the product, and the material selection may affect the durability and cost of the product. Finally, obtain the statistical analysis result, which provides in-depth insights into the design requirements of the target sheet metal part, including which parameters need special attention, and the potential problems and improvement directions that may need to be considered during the design process.

[0043] S13: Generate the design requirements and target design parameters of the target sheet metal part according to the statistical analysis result.

[0044] In this embodiment, analyze and statistically process the statistical analysis result to generate the design requirements of the target sheet metal part. For example, if the analysis result shows that the material type has a significant impact on the durability of the sheet metal part, then the design requirements may specify the use of a specific type of material; according to the statistical analysis result, it is possible to determine which parameters are the most critical and their optimal values or acceptable ranges. For example, if the analysis finds that a certain dimension is crucial for the assembly of the product, then this dimension may have strict tolerance requirements, and finally obtain the target design parameters.

[0045] Specifically, In one embodiment, such asFigure 3 As shown, in step S20, a feature extraction algorithm is used to extract the design requirements of the target sheet metal part to obtain demand feature point data, specifically including: S21: Apply an edge detection algorithm to identify the geometric contours and key structures in the design requirements of the target sheet metal part.

[0046] Specifically, through the edge detection algorithm, edge information can be extracted from the two-dimensional drawing or three-dimensional model of the sheet metal part. For example, edge detection can be used to identify the outer contour of the part, internal holes, bending lines, etc., and finally obtain the geometric contours and key structures in the design requirements of the target sheet metal part.

[0047] S22: Adopt shape matching technology to extract the standardized features in the design requirements of the target sheet metal part.

[0048] Specifically, adopt shape matching technology to extract the standardized features in the design requirements of the target sheet metal part. By analyzing the shape features of the target object in the image, such as hole positions, bending lines, edges, etc., and matching them with predefined templates or known shapes, high-precision target positioning and recognition can be achieved.

[0049] S23: Normalize the geometric contours, key structures, and standardized features to obtain demand feature point data.

[0050] Specifically, normalize the geometric contours, key structures, and standardized features. For example, use Min-Max normalization or Z-Score normalization to process the geometric contours, key structures, and standardized features. Finally, demand feature point data is obtained. After normalization, the obtained demand feature point data is a set of standardized values, which represent the key features in the design requirements of the target sheet metal part and are used for subsequent design verification, optimization, and manufacturing processes.

[0051] In an embodiment, as Figure 4 shown, before step S30, that is, before inputting the demand feature point data into a pre-trained design model for analysis to obtain the design parameter template of the initial sheet metal part, this sheet metal series parameterization design method further includes: S301: Obtain data of various types of standard sheet metal parts, preprocess and label the data of various types of standard sheet metal parts to obtain a training set.

[0052] Specifically, data of standard sheet metal parts are collected from different sources and examples. This data may include the dimensions of the parts, material types, processing technology requirements, functional requirements, etc. The collected data is subjected to preprocessing operations such as cleaning, formatting, and standardization to improve data quality. At the same time, the data is labeled, that is, specific features or patterns in the data are identified and classified. The preprocessed and labeled data is organized into a training set, which will be used to train a machine learning model. S302: Use the training set to perform forward propagation and backpropagation training on the design model constructed based on a deep learning neural network to obtain the design model after forward propagation and backpropagation training.

[0053] Specifically, use the preprocessed and labeled training set to train a deep learning neural network model. Through training, the neural network model learns how to extract features from the input data and predict the output results based on these features. In the forward propagation stage, the input data is passed layer by layer through the various levels of the neural network. For example, from the input layer to the hidden layer and then to the output layer. Each layer will perform certain processing on the data, usually by applying weights and activation functions. Backpropagation calculates the loss function, such as mean squared error or cross-entropy loss, and the gradients with respect to the network parameters, and then uses these gradients to update the model's parameters. After multiple iterations of forward propagation and backpropagation training, the parameters of the neural network model are adjusted to adapt to the training data, and finally, the design model after forward propagation and backpropagation training is obtained.

[0054] S303: Use a genetic algorithm to optimize the parameters of the design model after forward propagation and backpropagation training to obtain a pre-trained design model.

[0055] Specifically, use a genetic algorithm to optimize the parameters of the design model after forward propagation and backpropagation training. The genetic algorithm finds the optimal or sub-optimal solution of the neural network parameters by simulating biological genetic mechanisms such as natural selection, crossover (mating and recombination), and mutation, thereby improving the performance of the model and optimizing the parameters of the neural network model. After the neural network model is optimized by the genetic algorithm, its parameters are adjusted to better adapt to the training data, thereby improving the accuracy and reliability of the model in practical applications. Finally, a pre-trained design model is obtained.

[0056] In one embodiment, as Figure 5 shown, in step S30, the demand feature point data is input into the pre-trained design model for analysis to obtain the design parameter template of the initial sheet metal part, specifically including: S31: Receive the demand feature point data and perform preliminary data preprocessing on the demand feature point data to obtain the preprocessed feature point data.

[0057] Specifically, the design model first needs to receive the demand feature point data, which provides the necessary input information for the model. This information includes the key parameters and features of the design requirements, such as dimensions, materials, functional requirements, etc. The received demand feature point data is preliminarily preprocessed, such as data cleaning, data transformation, etc., to prepare the data for subsequent analysis or model training. Finally, the preprocessed feature point data is obtained. The preprocessed data becomes more suitable for the input requirements of the model, improving the quality and consistency of the data.

[0058] S32: Perform deep feature extraction and pattern recognition on the preprocessed feature point data through a multi-layer neural network to obtain the parameter pattern of the feature points.

[0059] Specifically, the preprocessed feature point data is input into a multi-layer neural network. Each layer of the network will transform and combine the data to extract higher-level feature representations. In the hidden layer of the neural network, through the activation function of the neurons and the adjustment of the weights, the network can identify the patterns and associations in the input data. After being processed by the multi-layer neural network, the finally output is the parameter pattern of the feature points. Pattern recognition enables the network to understand and classify the input data. For example, it can recognize specific shapes, textures, or other visual patterns.

[0060] S33: Analyze and design the parameter pattern of the feature points to obtain the design parameter template of the initial sheet metal part.

[0061] Specifically, using the parameter pattern obtained from the neural network, analyze and design to generate the design parameter template of the initial sheet metal part. This template contains all the key parameters and settings required for designing the sheet metal part. The design parameter template provides a starting point for subsequent detailed design and manufacturing, ensuring that the design meets the functional requirements and manufacturing constraints.

[0062] In one embodiment, as Figure 6 shown, in step S40, that is, according to the design parameter template of the initial sheet metal part and the target design parameters, perform 3D modeling to obtain the 3D model of the initial sheet metal part, specifically including: S41: Based on the CAD software platform, import the design parameter template of the initial sheet metal part to generate a preliminary 3D model.

[0063] Specifically, based on the CAD software platform, import the design parameter template of the initial sheet metal part. The design parameter template contains all the initial parameters required for generating the 3D model of the sheet metal part, such as dimensions, shapes, material properties, bending radii, etc. Using the imported design parameter template, the CAD software can automatically or semi-automatically generate a preliminary 3D model of the sheet metal part.

[0064] S42: Apply parametric modeling technology to dynamically adjust the geometry and dimensions of the preliminary 3D model based on the target design parameters, integrate finite element analysis, evaluate the structural strength and process feasibility of the 3D model, optimize the design parameters, and finally obtain the 3D model of the initial sheet metal part.

[0065] Specifically, apply parametric modeling technology to dynamically adjust the geometry and dimensions of the preliminary 3D model based on the target design parameters. Through parametric modeling, designers can modify the design parameters in real time, and the model will be automatically updated. Integrate finite element analysis to evaluate the structural strength and process feasibility of the 3D model. During the parametric modeling process, finite element analysis is embedded in the design process. Through finite element analysis, evaluate the structural performance of the 3D model under the expected load to ensure that it meets the strength and safety requirements, and analyze the manufacturing process of the model, including material forming, cutting, bending, welding and other process steps, to optimize the design parameters, and finally obtain the 3D model of the initial sheet metal part.

[0066] In one embodiment, as Figure 7 shown, this method for serial parametric design of sheet metal parts further includes: S70: Record the log information of each design modification, and optimize and adjust the pre-trained design model according to the log information.

[0067] Specifically, record the specific date and time of each design modification, and detail the specific content involved in this modification, such as the changed parameters, added or deleted components, geometric shape adjustments, etc.; record the reason for this modification, such as customer requirement changes, function optimization, error correction, etc.; extract the design modification records from the log system as the dataset for model training, use the historical design modification data to preliminarily train the model, learn the rules and patterns of design modifications, and adopt an algorithm that supports online learning to enable the model to be updated in real time when new data arrives without having to retrain the entire model, so as to improve the accuracy and reliability of the design and reduce errors and defects.

[0068] S80: Obtain the operation habits and preference data of designers, analyze the operation habits and preference data of designers using big data analysis technology to obtain the preference analysis results, and dynamically adjust the interface layout and function modules based on the preference analysis results to obtain a customized design environment.

[0069] Specifically, record the behavior patterns, frequently used functions, interface usage habits, etc. of designers when using design software or tools. Adopt big data analysis techniques, such as data mining, machine learning, statistical analysis, etc., to deeply analyze the collected operation habit and preference data, identify patterns such as designers' frequently used operations, preference settings, and common design processes, and obtain preference analysis results. For example, the frequently used toolbar layout, frequently used shortcut keys, frequently accessed function modules, etc. According to the analysis results, automatically or semi-automatically adjust the interface layout and function modules of the design software to better conform to the usage habits and preferences of designers. For example, place frequently used tools in more prominent positions, hide infrequently used functions, or adjust the color and theme of the interface to adapt to designers' preferences. Through dynamic adjustment, create a customized design environment so that designers can carry out design work more efficiently and comfortably.

[0070] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0071] In one embodiment, a sheet metal serialization and parameterization design system is provided. This sheet metal serialization and parameterization design system corresponds one-to-one with the sheet metal serialization and parameterization design method in the above embodiment. As Figure 8 shown, this sheet metal serialization and parameterization design system includes a requirement acquisition module, a feature extraction module, a model analysis module, a 3D construction module, a simulation evaluation module, and a generation and export module. The detailed description of each functional module is as follows: The requirement acquisition module is used to acquire the design information of the target sheet metal part, analyze the design information of the target sheet metal part based on Bayes' theorem, and obtain the design requirements and target design parameters of the target sheet metal part; The feature extraction module is used to extract features from the design requirements of the target sheet metal part by using a feature extraction algorithm to obtain requirement feature point data; The model analysis module is used to input the requirement feature point data into a pre-trained design model for analysis to obtain the design parameter template of the initial sheet metal part; The 3D construction module is used to perform 3D modeling according to the design parameter template of the initial sheet metal part and the target design parameters to obtain the 3D model of the initial sheet metal part; The simulation evaluation module is used to import the 3D model of the initial sheet metal part into a virtual reality environment for interactive simulation and evaluation to obtain an evaluation result; The generation and export module is used to adjust the 3D model of the initial sheet metal part according to the evaluation result to obtain the design drawing of the target sheet metal part, and export the design drawing of the target sheet metal part to the user side.

[0072] Optionally, the sheet metal serialization and parameterization design system further includes: A training set acquisition module, configured to acquire standard sheet metal part data of different types, preprocess and label the standard sheet metal part data of different types to obtain a training set; A model training module, configured to perform forward propagation and backpropagation training on a design model constructed based on a deep learning neural network using the training set to obtain a design model after forward propagation and backpropagation training; A model optimization module, configured to optimize the parameters of the design model after forward propagation and backpropagation training using a genetic algorithm to obtain a pre-trained design model.

[0073] A record adjustment module, configured to record the log information of each design modification and optimize and adjust the pre-trained design model according to the log information; A customized environment module, configured to acquire the operation habits and preference data of designers, analyze the operation habits and preference data of designers using big data analysis technology to obtain a preference analysis result, and dynamically adjust the interface layout and function modules based on the preference analysis result to obtain a customized design environment.

[0074] Optionally, the requirement acquisition module includes: A design information collection sub-module, configured to collect the design information of the target sheet metal part input by the user through the user interface, where the design information of the target sheet metal part includes part dimensions, material type, processing technology requirements, and functional requirements; A statistical analysis sub-module, configured to apply a Bayesian classifier to perform probability statistical analysis on the design information of the target sheet metal part, identify key parameters and potential requirements in the design to obtain a statistical analysis result; An analysis result sub-module, configured to generate the design requirements and target design parameters of the target sheet metal part according to the statistical analysis result.

[0075] Optionally, the feature extraction module includes: An algorithm recognition sub-module, configured to apply an edge detection algorithm to recognize the geometric contour and key structure in the design requirements of the target sheet metal part; An extraction sub-module, configured to extract the standardized features in the design requirements of the target sheet metal part using shape matching technology; An integration sub-module, configured to perform normalization processing on the geometric contour, key structure, and standardized features to obtain demand feature point data.

[0076] Optionally, the model analysis module includes: A data reception sub-module, configured to receive the demand feature point data and perform preliminary data preprocessing on the demand feature point data to obtain preprocessed feature point data; A pattern recognition sub-module, which is used to perform deep feature extraction and pattern recognition on the preprocessed feature point data through a multi-layer neural network to obtain the parameter pattern of the feature points; An analysis and design sub-module, which is used to analyze and design the parameter pattern of the feature points to obtain the design parameter template of the initial sheet metal part.

[0077] Optionally, the 3D construction module includes: A modeling sub-module, which is used to import the design parameter template of the initial sheet metal part based on the CAD software platform to generate a preliminary 3D model; An evaluation and adjustment sub-module, which is used to apply parametric modeling technology to dynamically adjust the geometric shape and size of the preliminary 3D model based on the target design parameters, and integrate finite element analysis to evaluate the structural strength and process feasibility of the 3D model, optimize the design parameters, and finally obtain the 3D model of the initial sheet metal part.

[0078] For the specific limitations of a sheet metal serialization and parameterization design system, reference can be made to the limitations of a sheet metal serialization and parameterization design method in the above text, which will not be elaborated here. Each module in the above sheet metal serialization and parameterization design system can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor of the computer device in the form of hardware or independent of it, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0079] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 9 shown. The computer device includes a processor, a memory, a network interface, and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a sheet metal serialization and parameterization design method.

[0080] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented: Obtain the design information of the target sheet metal part, analyze the design information of the target sheet metal part based on Bayes' theorem to obtain the design requirements and target design parameters of the target sheet metal part; Use a feature extraction algorithm to extract features from the design requirements of the target sheet metal part to obtain demand feature point data; Input the demand feature point data into a pre-trained design model for analysis to obtain the design parameter template of the initial sheet metal part; Perform 3D modeling based on the design parameter template of the initial sheet metal part and the target design parameters to obtain the 3D model of the initial sheet metal part; Import the 3D model of the initial sheet metal part into a virtual reality environment for interactive simulation and evaluation to obtain an evaluation result; Adjust the 3D model of the initial sheet metal part according to the evaluation result to obtain the design drawing of the target sheet metal part, and export the design drawing of the target sheet metal part to the user side.

[0081] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: Obtain the design information of the target sheet metal part, analyze the design information of the target sheet metal part based on Bayes' theorem to obtain the design requirements and target design parameters of the target sheet metal part; Use a feature extraction algorithm to extract features from the design requirements of the target sheet metal part to obtain demand feature point data; Input the demand feature point data into a pre-trained design model for analysis to obtain the design parameter template of the initial sheet metal part; Perform 3D modeling based on the design parameter template of the initial sheet metal part and the target design parameters to obtain the 3D model of the initial sheet metal part; Import the 3D model of the initial sheet metal part into a virtual reality environment for interactive simulation and evaluation to obtain an evaluation result; Adjust the 3D model of the initial sheet metal part according to the evaluation result to obtain the design drawing of the target sheet metal part, and export the design drawing of the target sheet metal part to the user side.

[0082] Those of ordinary skill in the art can understand that all or part of the processes in the above-described embodiment methods can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0083] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be assigned to different functional units and modules according to needs, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0084] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A design method for sheet metal serialization and parameterization, characterized in that The described method for series and parametric design of sheet metal includes: Obtain the design information of the target sheet metal part, analyze the design information of the target sheet metal part based on Bayes' theorem, and obtain the design requirements and target design parameters of the target sheet metal part; Use a feature extraction algorithm to extract features from the design requirements of the target sheet metal part to obtain demand feature point data; Input the demand feature point data into a pre-trained design model for analysis to obtain a design parameter template for the initial sheet metal part; Perform 3D modeling based on the design parameter template of the initial sheet metal part and the target design parameters to obtain a 3D model of the initial sheet metal part; Import the 3D model of the initial sheet metal part into a virtual reality environment for interactive simulation and evaluation to obtain an evaluation result; Adjust the 3D model of the initial sheet metal part according to the evaluation result to obtain the design drawing of the target sheet metal part, and export the design drawing of the target sheet metal part to the user terminal.

2. A design method for a sheet metal serialization and parameterization, as claimed in claim 1, wherein The obtaining of the design information of the target sheet metal part, analyzing the design information of the target sheet metal part based on Bayes' theorem, and obtaining the design requirements and target design parameters of the target sheet metal part includes: Collect the design information of the target sheet metal part input by the user through the user interface, where the design information of the target sheet metal part includes part dimensions, material type, processing technology requirements, and functional requirements; Apply a Bayesian classifier to perform probability statistical analysis on the design information of the target sheet metal part, identify key parameters and potential requirements in the design, and obtain a statistical analysis result; Generate the design requirements of the target sheet metal part and the target design parameters according to the statistical analysis result.

3. A design method for a sheet metal serialization and parameterization, according to claim 1, characterized in that, The using of a feature extraction algorithm to extract features from the design requirements of the target sheet metal part to obtain demand feature point data includes: Apply an edge detection algorithm to identify the geometric contour and key structure in the design requirements of the target sheet metal part; Adopt a shape matching technique to extract the standardized features in the design requirements of the target sheet metal part; Normalize the geometric contour, the key structure, and the standardized features to obtain the demand feature point data.

4. A design method for a series of sheet metal parametric designs according to claim 1, characterized in that, Before the inputting of the demand feature point data into a pre-trained design model for analysis to obtain a design parameter template for the initial sheet metal part, the method for series and parametric design of sheet metal further includes: Obtain data of various types of standard sheet metal parts, preprocess and label the data of various types of standard sheet metal parts to obtain a training set; Use the training set to perform forward propagation and backpropagation training on a design model constructed based on a deep learning neural network to obtain a design model after forward propagation and backpropagation training; Use a genetic algorithm to optimize the parameters of the design model after forward propagation and backpropagation training to obtain the pre-trained design model.

5. A design method for a sheet metal serialization and parameterization, characterized in that, according to claim 1 The inputting of the demand feature point data into a pre-trained design model for analysis to obtain a design parameter template for the initial sheet metal part includes: Receive the demand feature point data, and perform preliminary data preprocessing on the demand feature point data to obtain preprocessed feature point data; Performing deep feature extraction and pattern recognition on the preprocessed feature point data through a multi-layer neural network to obtain the parameter pattern of the feature points; Analyzing and designing the parameter pattern of the feature points to obtain the design parameter template of the initial sheet metal part.

6. A design method for sheet metal serialization and parameterization according to claim 1, characterized in that, The three-dimensional modeling according to the design parameter template of the initial sheet metal part and the target design parameters to obtain the three-dimensional model of the initial sheet metal part includes: Based on the CAD software platform, importing the design parameter template of the initial sheet metal part to generate a preliminary three-dimensional model; Applying parametric modeling technology, dynamically adjusting the geometric shape and size of the preliminary three-dimensional model based on the target design parameters, and integrating finite element analysis to evaluate the structural strength and process feasibility of the three-dimensional model, optimizing the design parameters, and finally obtaining the three-dimensional model of the initial sheet metal part.

7. A design method for sheet metal serialization and parameterization according to claim 1, characterized in that The design method for sheet metal serialization and parameterization further includes: Recording the log information of each design modification and optimizing and adjusting the pre-trained design model according to the log information; Obtaining the operation habits and preference data of the designer, analyzing the operation habits and preference data of the designer by using big data analysis technology to obtain the preference analysis result, and dynamically adjusting the interface layout and function modules based on the preference analysis result to obtain a customized design environment.

8. A sheet metal series and parametric design system, characterized in that, The design system for sheet metal serialization and parameterization includes: A requirement acquisition module for acquiring the design information of the target sheet metal part, analyzing the design information of the target sheet metal part based on Bayes' theorem to obtain the design requirements and target design parameters of the target sheet metal part; A feature extraction module for extracting the feature points of the design requirements of the target sheet metal part by using a feature extraction algorithm to obtain the required feature point data; A model analysis module for inputting the required feature point data into a pre-trained design model for analysis to obtain the design parameter template of the initial sheet metal part; A three-dimensional construction module for performing three-dimensional modeling according to the design parameter template of the initial sheet metal part and the target design parameters to obtain the three-dimensional model of the initial sheet metal part; A simulation evaluation module for importing the three-dimensional model of the initial sheet metal part into a virtual reality environment for interactive simulation and evaluation to obtain an evaluation result; A generation and export module for adjusting the three-dimensional model of the initial sheet metal part according to the evaluation result to obtain the design drawing of the target sheet metal part and exporting the design drawing of the target sheet metal part to the user side.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it realizes the steps of the design method for sheet metal serialization and parameterization according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it realizes the steps of the design method for sheet metal serialization and parameterization according to any one of claims 1 to 7.

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