Multi-objective and multi-case reliability-based design optimization for tailor rolled blank (TRB) structures

Abstract

Light weight and crashworthiness signify two main challenges facing in vehicle industry, which often conflict with each other. In order to achieve light weight while improving crashworthiness, tailor rolled blank (TRB) has become one of the most potential lightweight technologies. To maximize the characteristics of TRB structures, structural optimization has been adopted extensively. Conventional optimization studies have mainly focused on a single loading case (SLC). In practice, however, engineering structures are often subjected to multiple loading cases (MLC), implying that the optimal design under a certain condition may no longer be an optimum under other loading cases. Furthermore, traditional deterministic optimization could become less meaningful or even unacceptable when uncertainties of design variables and noises of system parameters are present. To address these issues, a multi-objective and multi-case reliability-based design optimization (MOMCRBDO) was developed in this study to optimize the TRB hat-shaped structure. The radial basis function (RBF) metamodel was adopted to approximate the responses of objectives and constraints, the non-dominated sorting genetic algorithm II (NSGA-II), coupled with Monte Carlo Simulation (MCS), was employed to seek optimal reliability solutions. The optimal results show that the proposed method is not only capable of improving the reliability of Pareto solutions, but also enhancing the robustness under MLC.