Chapter 20
Multi-Objective Optimization
of Automotive Front Rail Based
on Surrogate Model and NSGA-II
Yiwei He, Wangdong Xu, and Fanruo Gu
Abstract With the rapid development of automotive industry, more and more attention has been paid to the lightweight and safety design. Crashworthiness optimization is an essential part in automotive design. In this study, a non-dominated sorting
genetic algorithm II (NSGA-II) based on Kriging model is proposed to optimize
the structure of automotive frontal rail to meet the requirements of crashworthiness
and lightweight. The material of frontal rail is mild steel, which performs well in
strength. Kriging surrogate model is employed to replace traditional finite element
model, which will reduce much computational time and improve the efficiency. Then
NSGA-II is applied to solve the multi-objective optimization problem. The results
illustrate that the Pareto optimal front obtained by NSGA-II exhibits good performance on convergence and diversity. And then the optimal scheme for design is
selected, the accuracy is proved to be high. Compared to baseline model, the optimized automotive frontal rail shows significant improvement on crashworthiness and
achieves 7.5% weight reduction.
Keywords NSGA-II · Kriging model · Lightweight · Pareto optimal front
20.1 Introduction
As is known, crashworthiness optimization problems are always important for automotive design. And plenty of researchers focus on thin-wall structure optimization.
Ma et al. had many researches of topology [1]. And there are many topics, such
Y. He (B)
Mechanical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
e-mail: YiweiHEEE@163.com
W. Xu
Mathematics and Computer Science, University of California-San Diego, La Jolla, San Diego, CA
92122, USA
F. Gu
Faculty of Arts and Sciences, College of William & Mary, Williamsburg, VA 23187, USA
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
J. Xu and K. M. Pandey (eds.), Mechanical Engineering and Materials,
Mechanisms and Machine Science 100,
https://doi.org/10.1007/978-3-030-68303-0_20
251
Multi-Objective Optimization
of Automotive Front Rail Based
on Surrogate Model and NSGA-II
Yiwei He, Wangdong Xu, and Fanruo Gu
Abstract With the rapid development of automotive industry, more and more attention has been paid to the lightweight and safety design. Crashworthiness optimization is an essential part in automotive design. In this study, a non-dominated sorting
genetic algorithm II (NSGA-II) based on Kriging model is proposed to optimize
the structure of automotive frontal rail to meet the requirements of crashworthiness
and lightweight. The material of frontal rail is mild steel, which performs well in
strength. Kriging surrogate model is employed to replace traditional finite element
model, which will reduce much computational time and improve the efficiency. Then
NSGA-II is applied to solve the multi-objective optimization problem. The results
illustrate that the Pareto optimal front obtained by NSGA-II exhibits good performance on convergence and diversity. And then the optimal scheme for design is
selected, the accuracy is proved to be high. Compared to baseline model, the optimized automotive frontal rail shows significant improvement on crashworthiness and
achieves 7.5% weight reduction.
Keywords NSGA-II · Kriging model · Lightweight · Pareto optimal front
20.1 Introduction
As is known, crashworthiness optimization problems are always important for automotive design. And plenty of researchers focus on thin-wall structure optimization.
Ma et al. had many researches of topology [1]. And there are many topics, such
Y. He (B)
Mechanical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
e-mail: YiweiHEEE@163.com
W. Xu
Mathematics and Computer Science, University of California-San Diego, La Jolla, San Diego, CA
92122, USA
F. Gu
Faculty of Arts and Sciences, College of William & Mary, Williamsburg, VA 23187, USA
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
J. Xu and K. M. Pandey (eds.), Mechanical Engineering and Materials,
Mechanisms and Machine Science 100,
https://doi.org/10.1007/978-3-030-68303-0_20
251