Multi-Objective Reliability-Based Optimization of Control Arm Using MCS and NSGA-II Coupled with Entropy Weighted GRA

Lightweight design is one of the important ways to reduce automobile fuel consumption and exhaust emissions. At same time, fatigue life parts also greatly affects vehicle safety. This paper proposes a multi-objective reliability optimization method by integrating Monte Carlo simulation (MCS) with NSGA-II algorithm coupled entropy weighted grey relational analysis (GRA) for lightweight lower control arm Macpherson suspension. The dynamic load histories were extracted through simulations rigid-flexible coupling model on virtual proving ground. Then, nominal stress was used predict its life. Six variables defined describe geometric dimension arm, while mass taken as objectives. carried out based Kriging surrogate algorithm. Aiming at uncertainty variables, constraint added improve arm. optimal determined from Pareto solutions (GRA). results show that reduced 4.1% increased 215.8% 7.8%. proposed proved be feasible effective suspension