Crashworthiness Optimization of Thin-walled Rail with Variable Section Based on Artificial Neural Network
Wu Hequan1; Xin Yong2
1. College of Automotive and Mechanical Engineering,Changsha University of Science and Technology,Changsha 410114, Hunan2. College of Mechanical and Electrical Engineering,Nanchang University,Nanchang 330031,Jiangxi
Abstract：In this paper, an effective structure of thin-walled rail with variable section was proposed by comparing a variety of thin-walled rails with different section features. Then the thin-walled rail was used in the front of a vehicle’s frame in order to improve its dynamic crashworthiness characteristics of energy absorption. Several design parameters of the structure have been used to optimize the rail’s structure. Optimal design method was presented and utilized to obtain optimal crashworthiness design of the thin-walled rail with variable section .The methodology adopted in this research made use of Design of Experiments (DOE), Finite Element Analysis (FEA), Artificial Neural Network (ANN) and Genetic Algorithms (GA). The forecasting model for energy absorption were created using ANN method exploiting finite element analysis results. ANN model is interfaced with GA method to find the optimal parameter values and the optimal energy absorption. Then the optimal results were verified through the finite element analysis of thin-walled rail. In the end, the optimal thin-walled rail with variable section was used in some vehicle’s 40% offset-barrier impact model and the passive safety of the vehicle was improved a lot.
2010, Vol. 29 
