Vehicle frame optimization method integrating Kriging model and analytic hierarchy process

Abstract

The invention relates to the technical field of automobile frame design, in particular to a Kriging model and analytic hierarchy process integrated frame optimization method, which comprises the following steps of: establishing an initial geometric model of a frame through computer aided design software, and performing structural performance analysis on the frame by using finite element analysis software to obtain initial structural performance data; based on the initial structure performance data, performing approximate modeling on the frame structure performance by using a Kriging model to form a performance prediction model; carrying out importance sorting and weight distribution on each key parameter in the frame design by adopting an analytic hierarchy process, and determining an optimized objective function and constraint conditions; in combination with an optimization algorithm, under the guidance of a Kriging model and analytic hierarchy process weight distribution, performing global search and optimization on frame design parameters; performing finite element analysis verification on the obtained optimal design scheme; according to the method, the Kriging model and the analytic hierarchy process are fused, so that global search and optimization of design parameters are realized, and the design efficiency is greatly improved.

Description

Technical Field

[0001] The invention relates to the technical field of automobile frame design, and in particular to a frame optimization method integrating a Kriging model with a hierarchy analysis method. Background Art

[0002] With the rapid development of the automobile industry, the performance of the frame, as an important load-bearing structure of the automobile, is directly related to the safety, comfort and economy of the automobile.

[0003] In the past, frame design methods were often based on experience and trial and error, which not only took a long time to design, but also resulted in poor design solutions. With the development of simulation model technology, its application in the optimization design of frames has become a research hotspot, improving the shortcomings of previous design methods.

[0004] However, as the complexity of engineering problems continues to increase, and frame design involves multiple parameters and multiple performance indicators, the size and complexity of simulation models are also showing an increasing trend. However, the time consumption of the solution process is doubling, which makes it a resource-intensive task to directly apply multi-objective optimization algorithms to actual simulation models with physical meaning. In actual engineering applications, this direct solution method may consume a lot of time and computing resources, thus limiting its application to complex engineering problems. Summary of the invention

[0005] The purpose of the present invention is to provide a frame optimization method that integrates the Kriging model and the hierarchical analysis method, and to solve the problems existing in the prior art by integrating the Kriging model and the hierarchical analysis method during frame design. In order to achieve the above purpose, the present invention solves the problem through the following technical solutions:

[0006] The present invention provides a frame optimization method integrating a Kriging model with a hierarchy analysis method, comprising the following steps:

[0007] S101, establishing an initial geometric model of the frame using computer-aided design software, and performing structural performance analysis on the frame using finite element analysis software to obtain initial structural performance data;

[0008] S102, based on the initial structural performance data, using the Kriging model to approximate the frame structural performance to form a performance prediction model;

[0009] S103, using the analytic hierarchy process to rank the importance and assign weights to the key parameters in the frame design, and determine the optimization objective function and constraint conditions;

[0010] S104, combining the optimization algorithm, under the guidance of the Kriging model and the weight allocation of the analytic hierarchy process, globally searching and optimizing the frame design parameters to find the optimal design solution;

[0011] S105. Perform finite element analysis to verify the optimal design solution to ensure that its structural performance meets the design requirements.

[0012] As a further technical solution, in step S105, an iterative feedback mechanism is set up. If the finite element verification results show that the design scheme does not meet the requirements, the design scheme needs to be adjusted and re-optimized. At the same time, the results of the finite element analysis are fed back to the Kriging model, and the Kriging model is continuously updated to improve the reliability of the optimization results.

[0013] As a further technical solution, in step S103, multiple optimization objectives are defined, including minimizing weight, maximizing stiffness, and minimizing cost.

[0014] As a further technical solution, in step S104, a multi-objective optimization algorithm is used to process multiple conflicting optimization objectives, so that a set of feasible solutions can be searched and found in the solution space to obtain the best design balance.

[0015] As a further technical solution, during global search and optimization, a reasonable search range and step size are set for the frame design parameters to fully explore the design space and find the optimal solution.

[0016] As a further technical solution, the multi-objective optimization algorithm selects one of a genetic algorithm, a particle swarm algorithm, and a simulated annealing algorithm.

[0017] As a further technical solution, in step S103, the relative importance of each key parameter in the frame design is determined, expressed as a judgment matrix, and a consistency check is performed; if the consistency index does not meet the requirements, the judgment matrix needs to be adjusted until the consistency requirements are met.

[0018] As a further technical solution, a weight vector is obtained by calculating the maximum eigenvalue of the judgment matrix and the corresponding eigenvector.

[0019] As a further technical solution, in step S102, appropriate Kriging basis functions and parameters are selected when constructing the Kriging model, so as to fit the frame structure performance data and global characteristics.

[0020] As a further technical solution, the Kriging basis function selects one of a linear function, a polynomial function and a Gaussian function.

[0021] The beneficial effects of the present invention are as follows:

[0022] (1) The Kriging model constructed by the present invention serves as a proxy model of the actual model. It has excellent prediction accuracy and can provide highly accurate model fitting in complex problems. It not only provides an effective approximation to the actual simulation model, but also has a computational efficiency that is much higher than directly solving the actual model, thereby achieving efficient solution.

[0023] (2) The present invention combines a multi-objective optimization algorithm, under the guidance of the Kriging model and the weight distribution of the analytic hierarchy process, to achieve global optimization of the frame design parameters, greatly shortening the design cycle and reducing costs. At the same time, it can automatically search and optimize the design parameters, avoiding a large number of trial and error and iteration processes in traditional methods, and improving design efficiency.

[0024] (3) The present invention adopts the hierarchical analysis method to rank the importance and assign weights to the key parameters in the frame design. Multiple sets of data can be formed based on the weights of the key parameters. In the subsequent multi-objective optimization process, a global search is performed on the multiple sets of data, avoiding the traditional large-scale iterative trial and error process.

[0025] (4) The Kriging model in the present invention can accurately predict the structural performance of the frame and provide reliable data support for optimization. At the same time, the weight allocation of the hierarchical analysis method can ensure the rationality and scientificity of the optimization target, avoid the influence of subjective factors on the optimization results, and improve the accuracy of the optimization. The subsequent finite element analysis verification and feedback mechanism further ensure the practicality and reliability of the optimization results.

[0026] (5) The present invention can continuously improve and optimize the Kriging model through optimization result verification and feedback, thereby improving its prediction accuracy and reliability. Ultimately, a frame design scheme with excellent performance and meeting design requirements can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The illustrative embodiments of the present invention and their description are used to explain the present invention and do not constitute a limitation of the present invention. It should also be understood that these drawings are shown for simplicity and clarity and are not necessarily drawn to scale. The present invention will now be described and explained with additional features and details through the use of the accompanying drawings, in which:

[0028] Figure 1 A flow chart of a vehicle frame optimization method combining the Kriging model with the analytic hierarchy process in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0029] The technical solutions in typical embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0030] like Figure 1 As shown, this embodiment provides a frame optimization method integrating Kriging model and hierarchical analysis method, comprising the following steps:

[0031] S101. Establish the initial design model of the frame and obtain initial structural performance data

[0032] The initial geometric model of the frame is established through computer-aided design software, and the structural performance of the frame is analyzed using finite element analysis software to obtain initial structural performance data.

[0033] When establishing the initial geometric model, it is necessary to consider the actual working conditions and usage requirements of the frame to ensure the authenticity and reliability of the model. The initial structural performance data obtained can provide a basis for the subsequent Kriging model establishment and optimization.

[0034] S102. Construct Kriging model based on initial structural performance data

[0035] Based on the initial structural performance data, the Kriging model is used to approximate the frame structure performance and form a performance prediction model, which can approximately describe the relationship between the frame structure performance and design parameters. The Kriging model is a prediction model based on statistical learning theory, which can capture the local and global characteristics of the data and provide efficient and accurate performance prediction for subsequent optimization.

[0036] When constructing the Kriging model, appropriate Kriging basis functions and parameters are selected so as to fit the frame structure performance data and global characteristics to improve the prediction accuracy of the Kriging model for the frame structure performance.

[0037] The Kriging basis function selects one of the linear function, polynomial function and Gaussian function. These basis functions can fit the frame structure performance data and global characteristics, thereby reducing the computational cost and improving the optimization efficiency in the subsequent optimization process.

[0038] At the same time, the Kriging model needs to be verified and adjusted through cross-validation, residual analysis and other methods to ensure the reliability and applicability of the model.

[0039] S103, using the analytic hierarchy process to assign weights to key parameters of frame design

[0040] The key parameters of frame design generally include seat height, frame width, front length, seat length, rear length, front suspension height, and rear suspension height. The AHP method is used to rank the importance of the key parameters in frame design and assign weights to determine the objective function and constraints of the optimization. The AHP method is a decision-making analysis method based on expert experience and mathematical models that can handle the complex relationship between multiple factors. The weight allocation of the AHP method can ensure the rationality and scientificity of the optimization target, avoid the influence of subjective factors on the optimization results, and improve the accuracy of the optimization.

[0041] The frame design is complex and multiple optimization targets need to be defined. The multiple optimization targets defined in this embodiment include minimizing weight, maximizing stiffness, and minimizing cost. It is understood that in some other embodiments, other optimization targets may be selected, not limited to the optimization targets in this embodiment, and may be selected according to actual needs.

[0042] Specifically, the relative importance of each key parameter in the frame design is determined and expressed as a judgment matrix. The weight vector is obtained by calculating the maximum eigenvalue and the corresponding eigenvector of the judgment matrix. The weight vector reflects the influence of each parameter on the optimization target.

[0043] In order to ensure the rationality of weight distribution, a consistency check is also required to check whether the consistency index of the judgment matrix meets the predetermined threshold. If the consistency index does not meet the requirements, the judgment matrix needs to be adjusted until the consistency requirements are met. Through consistency testing, a reasonable and scientific weight distribution can be obtained, providing accurate guidance for subsequent global optimization searches.

[0044] If necessary, weight allocation can be assisted by tools such as expert scoring and analytic hierarchy process software. At the same time, the weights need to be adjusted and optimized according to actual conditions to meet design requirements and optimization goals.

[0045] S104. Combine multi-objective optimization methods to perform global optimization search

[0046] The multiple optimization objectives defined in step S103 are often conflicting and contradictory, and the optimal design solution can only be obtained after balancing the optimization objectives. This embodiment combines the multi-objective optimization algorithm, under the guidance of the Kriging model and the weight allocation of the hierarchical analysis method, to perform a global search and optimization of the frame design parameters to find the optimal design solution.

[0047] Multi-objective optimization algorithms can handle multiple conflicting optimization objectives and search and find a set of feasible solutions in the solution space to obtain the best design balance.

[0048] The multi-objective optimization algorithm of this embodiment selects one of the genetic algorithm, particle swarm algorithm, and simulated annealing algorithm. The above algorithm can search and find a set of feasible solutions in the solution space, and these solutions achieve a good balance in the optimization objectives. By comparing the advantages and disadvantages of these solutions, the most satisfactory design solution can be selected as the final optimization result.

[0049] When performing global search and optimization, a reasonable search range and step size are set for the frame design parameters to fully explore the design space and find the optimal solution. In addition, the multi-objective optimization algorithm can be adjusted and optimized to improve the search efficiency and accuracy, such as using strategies such as adaptive adjustment of step size and crossover probability.

[0050] This embodiment combines a multi-objective optimization algorithm, under the guidance of the Kriging model and the weight distribution of the analytic hierarchy process, to achieve global optimization of the frame design parameters, greatly shortening the design cycle and reducing costs. It can also automatically search and optimize the design parameters, avoiding a large number of trial and error and iterative processes in traditional methods and improving design efficiency.

[0051] S105. Optimization result verification and feedback

[0052] The optimal design solution is verified by finite element analysis to ensure that its structural performance meets the design requirements. An iterative feedback mechanism is set up. If the finite element verification results show that the design solution does not meet the requirements, the verification results need to be analyzed and evaluated in detail to find potential problems and deficiencies, and the design solution needs to be adjusted and optimized as necessary based on the evaluation results to ensure the practicality and reliability of the final design. At the same time, the results of the finite element analysis are fed back to the Kriging model, and the Kriging model is continuously updated to improve the reliability of the optimization results.

[0053] Through optimization result verification and feedback, the Kriging model can be continuously improved and optimized to improve its prediction accuracy and reliability. Ultimately, a frame design with excellent performance and meeting design requirements can be obtained.

[0054] Although the present invention has been disclosed as above in the form of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications to the technical solution of the present invention by using the methods and technical contents disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the protection scope of the technical solution of the present invention.

Claims

1. A frame optimization method integrating Kriging model and analytic hierarchy process, characterized in that: The following steps are involved: S101, establishing an initial geometric model of the frame using computer-aided design software, and performing structural performance analysis on the frame using finite element analysis software to obtain initial structural performance data; S102, based on the initial structural performance data, using the Kriging model to approximate the frame structural performance to form a performance prediction model; S103, using the analytic hierarchy process to rank the importance and assign weights to the key parameters in the frame design, and determine the optimization objective function and constraint conditions; S104, combining the optimization algorithm, under the guidance of the Kriging model and the weight allocation of the analytic hierarchy process, globally searching and optimizing the frame design parameters to find the optimal design solution; S105. Perform finite element analysis to verify the optimal design solution to ensure that its structural performance meets the design requirements.

2. The frame optimization method combining Kriging model and analytic hierarchy process as claimed in claim 1 is characterized in that: In step S105, an iterative feedback mechanism is set up. If the finite element verification results show that the design scheme does not meet the requirements, the design scheme needs to be adjusted and re-optimized. At the same time, the results of the finite element analysis are fed back to the Kriging model, and the Kriging model is continuously updated to improve the reliability of the optimization results.

3. The frame optimization method combining Kriging model and analytic hierarchy process as claimed in claim 1, characterized in that: In step S103 , multiple optimization objectives are defined, including minimizing weight, maximizing stiffness, and minimizing cost.

4. The frame optimization method combining Kriging model and analytic hierarchy process as claimed in claim 3 is characterized in that: In step S104, a multi-objective optimization algorithm is used to process multiple conflicting optimization objectives, and a set of feasible solutions can be searched and found in the solution space to obtain the best design balance.

5. The frame optimization method combining Kriging model and analytic hierarchy process as claimed in claim 4 is characterized in that: When performing global search and optimization, a reasonable search range and step size are set for the frame design parameters to fully explore the design space and find the optimal solution.

6. The frame optimization method combining Kriging model and analytic hierarchy process as claimed in claim 4 is characterized in that: The multi-objective optimization algorithm is selected from one of the genetic algorithm, particle swarm algorithm, and simulated annealing algorithm.

7. The frame optimization method combining Kriging model and analytic hierarchy process as claimed in claim 1 is characterized in that: In step S103, the relative importance of each key parameter in the frame design is determined, expressed as a judgment matrix, and a consistency check is performed; if the consistency index does not meet the requirements, the judgment matrix needs to be adjusted until the consistency requirements are met.

8. The frame optimization method combining Kriging model and analytic hierarchy process as claimed in claim 7, characterized in that: The weight vector is obtained by calculating the maximum eigenvalue and the corresponding eigenvector of the judgment matrix.

9. The frame optimization method combining Kriging model and analytic hierarchy process as claimed in claim 1, characterized in that: In step S102, appropriate Kriging basis functions and parameters are selected when constructing the Kriging model so as to fit the frame structure performance data and global characteristics.

10. The frame optimization method combining Kriging model and analytic hierarchy process as claimed in claim 9, characterized in that: The Kriging basis function is selected from one of the linear function, polynomial function and Gaussian function.

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