Research on optimization of vehicle anti-shock protection components based on neural network

LI Mingxing; WANG Xianhui; ZHOU Yunbo; SUN Xiaowang; ZENG Bin; HU Wenhai

doi:10.11883/bzycj-2019-0055

Volume 40 Issue 2

Jan. 2020

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Article Navigation > Explosion And Shock Waves > 2020 > 40(2): 024203

LI Mingxing, WANG Xianhui, ZHOU Yunbo, SUN Xiaowang, ZENG Bin, HU Wenhai. Research on optimization of vehicle anti-shock protection components based on neural network[J]. Explosion And Shock Waves, 2020, 40(2): 024203. doi: 10.11883/bzycj-2019-0055

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LI Mingxing, WANG Xianhui, ZHOU Yunbo, SUN Xiaowang, ZENG Bin, HU Wenhai. Research on optimization of vehicle anti-shock protection components based on neural network[J]. Explosion And Shock Waves, 2020, 40(2): 024203. doi: 10.11883/bzycj-2019-0055

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Research on optimization of vehicle anti-shock protection components based on neural network

doi: 10.11883/bzycj-2019-0055

1.
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China
2.
Technical Center of Dongfeng Motor Corporation, Wuhan 430056, Hubei, China

Received Date: 2019-02-27
Rev Recd Date: 2019-06-20

Available Online: 2019-12-25

Publish Date: 2020-02-01

Abstract

Abstract

With the increasing requirements for the protection of military vehicles, the design of impact protection components is facing more and more challenges. In order to provide an efficient and scientific research method, this paper adopts a V-shaped structure, and uses radial basis function neural network approximation model and multi-objective genetic algorithm to optimize the design of a certain type of vehicle protection components. Taking the deformation amount of the protection component and the total mass as the design goal, the sensitivity analysis is used to select the design factor that has a great influence on the protection performance of the protection component. The approximate model of the experimental design sample is constructed by radial basis function neural network, and then multi-objective genetic algorithm is used to numerically optimize the optimal component of the protection component. Finally, through simulation and experimental verification, it is proved that the optimization scheme meets the design requirements. Provide a design idea for the future development of protective components.
- protective component,
- V-shaped structure,
- sensitivity analysis,
- neural network,
- multi-objective genetic algorithm

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References(19)

References

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