An adaptive RBF neural network–based multi-objective optimization method for lightweight and crashworthiness design of cab floor rails using fuzzy subtractive clustering algorithm

To improve the computational cost and accuracy of approximate-based multi-objective optimization problems in engineering, an adaptive RBF neural network (ARBFNN) method integrating RBF neural network model, fuzzy subtractive clustering (FSC) sequence sampling method and non-dominated sorting genetic...

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Vydané v:Structural and multidisciplinary optimization Ročník 63; číslo 2; s. 915 - 928
Hlavní autori: Wang, Dengfeng, Xie, Chong, Wang, Shuang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2021
Springer Nature B.V
Predmet:
Accuracy
Approximation
Artificial neural networks
Clustering
Computational Mathematics and Numerical Analysis
Crashworthiness
Design optimization
Efficiency
Engineering
Engineering Design
Finite element method
Floors
Fuzzy logic
Genetic algorithms
Impact strength
Industrial Application Paper
Lightweight
Multiple objective analysis
Neural networks
Nonlinear analysis
Optimization
Rails
Sampling methods
Sorting algorithms
Theoretical and Applied Mechanics
Coupled cab
Multi-objective optimization
Fuzzy subtraction clustering sequence sampling
Lightweight and crashworthiness design
Adaptive RBF neural network
ISSN:1615-147X, 1615-1488
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Popis
Shrnutí:To improve the computational cost and accuracy of approximate-based multi-objective optimization problems in engineering, an adaptive RBF neural network (ARBFNN) method integrating RBF neural network model, fuzzy subtractive clustering (FSC) sequence sampling method and non-dominated sorting genetic algorithm (NSGA-II) is proposed. To solve the problem that the number of new sample points is difficult to determine, FSC sequence sampling is proposed to select the clustering center points as newly added sample points. First, the ARBFNN method is verified by the five test functions. The results show that the ARBFNN method is significantly better than static RBFNN model based on multi-objective optimization (SRBFNN) in terms of global convergence and efficiency performance, and the other two adaptive approximate optimization methods in terms of overall efficiency. Based on the above, the accuracy and efficiency of ARBFNN method are very high. Finally, the method is applied to an engineering example: the lightweight and crashworthiness design of floor rails. The cab model coupled with implicit parameterized floor rails model is built using the SFE-CONCEPT software to achieve collaborative optimization design of shape-size-material. The optimization results show that the ARBFNN method can guarantee the error of the approximate optimal solution and the finite element solution (expensive solution) within 2%, so the accuracy of highly nonlinear (finite element analysis of collision conditions) approximate optimization is improved. Hence, the proposed ARBFNN method is feasible and effective in solving complex and expensive engineering optimization problems.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-020-02797-9