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

Turn off MathJax

Article Contents

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

Citation:

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

Citation:

PDF( 1034 KB)

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

FullText(HTML)

References(19)

References

[1]	王显会, 佘磊, 郭启涛, 等. 基于抗冲击波响应的新型蜂窝夹层结构多目标优化设计 [J]. 车辆与动力技术, 2014(4): 25–30. WANG X H, YAN L, GUO Q T, et al. Multi-objective optimization design of new honeycomb sandwich structure based on shock wave response [J]. Vehicle & Power Technology, 2014(4): 25–30.
[2]	张钱城, 郝方楠, 李裕春, 等. 爆炸冲击载荷作用下车辆和人员的损伤与防护 [J]. 力学与实践, 2014, 36(5): 527–539. DOI: 10.6052/1000-0879-13-539. ZHANG Q C, HAO F N, LI Y C, et al. Damage and protection of vehicles and personnel under blast loading [J]. Mechanics in Engineering, 2014, 36(5): 527–539. DOI: 10.6052/1000-0879-13-539.
[3]	KENDALE A, AMERICAS T, JATEGAONKAR R, et al. Study of occupant responses in a mine blast using MADYMO [C] // SAFE Symposium, 2009.
[4]	张中英, 何洋扬, 王乐阳, 等. 车底结构对爆炸冲击波响应特性影响研究[C] // 全国仿真技术学术会议. 九江, 2009. 123−126.
[5]	孙京帅. 蜂窝材料面内冲击吸能性能优化及在电动汽车耐撞性设计中的应用[D]. 大连: 大连理工大学, 2013.
[6]	FENG H M. Self-generation RBFNs using evolutional PSO learning [J]. Neurocomputing, 2006, 70(1-3): 241–251. DOI: 10.1016/j.neucom.2006.03.007.
[7]	柳建容, 马咏梅, 黄巍. 基BP神经网络与遗传算法的减振器优化设计 [J]. 机械科学与技术, 2011(8): 1267–1271. LIU J R, MA Y M, HUANG W. Optimal design of shock absorber based on bp neural network and genetic algorithm [J]. Mechanical Science and Technology, 2011(8): 1267–1271.
[8]	李利莎, 谢清粮, 郑全平, 等. 基于Lagrange、ALE和SPH算法的接触爆炸模拟计算 [J]. 爆破, 2011, 28(1): 18–22. DOI: 10.3963/j.issn.1001-487X.2011.01.005. LI L S, XIE Q L, ZHENG Q P, et al. Simulation of contact explosion based on Lagrange, ALE and SPH algorithms [J]. Blasting, 2011, 28(1): 18–22. DOI: 10.3963/j.issn.1001-487X.2011.01.005.
[9]	方开泰. 均匀实验设计的理论、方法和应用——历史回顾 [J]. 数理统计与管理, 2004, 23(3): 69–80. DOI: 10.3969/j.issn.1002-1566.2004.03.016. FANG K T. Theory, method and application of uniform experimental design—historical review [J]. Journal of Mathematical Statistics and Management, 2004, 23(3): 69–80. DOI: 10.3969/j.issn.1002-1566.2004.03.016.
[10]	SOBOL I M. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates [M]. Elsevier Science Publishers, 2001. DOI: 10.1016/S0378-4754(00)00270-6.
[11]	CHEN S, WANG X X, BROWN D J. Sparse incremental regression modeling using correlation criterion with boosting search [J]. IEEE Signal Processing Letters, 2005, 12(3): 198–201. DOI: 10.1109/LSP.2004.842250.
[12]	CHEN S, WOLFGANG A, HARRIS C J, et al. Symmetric RBF classifier for nonlinear detection in multiple-antenna-aided systems [J]. IEEE Transactions on Neural Networks, 2008, 19(5): 737. DOI: 10.1109/TNN.2007.911745.
[13]	GONZALEZ J, ROJAS I, ORTEGA J, et al. Multiobjective evolutionary optimization of the size, shape, and position parameters of radial basis function networks for function approximation [J]. IEEE Transactions on Neural Networks, 2003, 14(6): 1478–1495. DOI: 10.1109/TNN.2003.820657.
[14]	LEUNG F H F, LAM H K, LING S H, et al. Tuning of the structure and parameters of a neural network using an improved genetic algorithm [J]. IEEE Transactions on Neural Networks, 2003, 14(1): 79–88. DOI: 10.1109/TNN.2002.804317.
[15]	BORS A G, PITAS I. Median radial basis function neural network [J]. IEEE Transactions on Neural Networks, 1996, 7(6): 1351–1364. DOI: 10.1109/72.548164.
[16]	YIN H, ALLINSON N M. Self-organizing mixture networks for probability density estimation [J]. IEEE Transactions on Neural Networks, 2001, 12(2): 405–411. DOI: 10.1109/72.914534.
[17]	RAJEEV S, KRISHNAMOORTHY C S. Genetic algorithm-based methodology for design optimization of reinforced concrete frames [J]. Computer-Aided Civil and Infrastructure Engineering, 2002, 13(1): 63–74. DOI: 10.1111/0885-9507.00086.
[18]	魏然, 王显会, 周云波, 等. 帕累托最优在车辆底部防护结构设计中的应用研究 [J]. 兵工学报, 2015, 36(6). DOI: 10.3969/j.issn.1000-1093.2015.06.013. WEI R, WANG X H, ZHOU Y B, et al. Research on the application of Pareto optimality in the protection structure design of vehicle bottom [J]. Acta Armamentarii, 2015, 36(6). DOI: 10.3969/j.issn.1000-1093.2015.06.013.
[19]	蔡亚. 多目标遗传算法的改进及其变速箱参数优化设计研究[D]. 合肥: 合肥工业大学, 2014. DOI: CNKI:CDMD:2.1015.568685.