Journal of South China University of Technology(Natural Science Edition) ›› 2025, Vol. 53 ›› Issue (11): 132-140.doi: 10.12141/j.issn.1000-565X.240364

• Vehicle Engineering • Previous Articles     Next Articles

Time Series Prediction of Vehicle Crash Based on Process Parameters Analysis of Friction Stir Welding

XIE Zhengchao1, LIU Jincan1, LI Shuang1, LI Wenfeng2, ZHAO Jing2   

  1. 1.School of Mechanical and Automotive Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China
    2.School of Mechanical Engineering and Automation,Northeastern University,Shenyang 110819,Liaoning,China
  • Received:2024-07-15 Online:2025-11-25 Published:2025-05-30
  • Contact: 赵晶(1987—),男,博士,教授,主要从事整车有限元分析与整车动力学研究。 E-mail:zhaoj@mail.neu.edu.cn
  • About author:谢正超(1978—),男,教授,博士生导师,主要从事有限元数值分析、车辆动力学分析与控制等研究。E-mail: zxie@scut.edu.cn
  • Supported by:
    the National Key Research and Development Program of China(2019YFE0110700)

Abstract:

Under the demands for vehicle lightweighting and high safety, friction stir welding has become the core technology for the manufacturing of key vehicle body structures, and it is crucial for the enhancement of collision energy absorption capacity. However, when the traditional explicit dynamic model is used to optimize the process pa-rameters of friction stir welding, a large number of repeated finite element calculations are required, thus leading to such problems as long calculation time and high resource consumption, which restricts the design efficiency. To solve these problems, this paper proposes a time series prediction method of vehicle crash based on the process parameters analysis of friction stir welding, which balances the optimization efficiency and crash safety. During the investigation, first, the mapping relationships between rotational speed, welding speed and the elastic modulus of welded parts are summarized, and a parameter set is constructed. Next, by taking a body-in-white as the object, the set of body-in-white components with mixed shell elements is discretized and the frontal crash condition is set to establish a vehicle explicit dynamic model. Then, a time series prediction surrogate model is designed, and is trained with explicit dynamic response data, with the combination of a high-dimension data decoupler and a penalty function, thus finally forming a surrogate model update process toward the goal of minimizing the deformation and strain of observation points. After iteration, the root mean square error and loss of the surrogate model’s prediction results approach zero, which means that the model accuracy is reliable. In addition, compared with the traditional method, the proposed method saves 50% of the calculation time. This method achieves the collaborative optimization of lightweighting and high safety of vehicles, provides an efficient technical means for vehicle body design and the iteration of friction stir welding process parameters, and has engineering value for shortening the research and deve-lopment cycle and improving the safety performance of vehicles.

Key words: vehicle body design, vehicle impact safety, friction stir welding, process optimization, surrogate network model, finite element method

CLC Number: