Lightweight optimization of the front end structure of an automobile body using entropy-based grey relational analysis

This study deals with the multi-objective lightweight optimization of the front end structure of an automobile body, as the main assembly to withstand impact force and protect occupants from injuries in frontal collision, based on entropy-based grey relational analysis (EGRA). First, basic noise, vibration, and harshness (NVH) models of the automobile body and crashworthiness models of the vehicle are established and then validated by corresponding actual experiments; hence the lightweight controlling quotas are extracted. Next, the contribution analysis method determines the final parts for lightweight optimization, for which both continuous thickness variables and discrete material variables are simultaneously taken into account. Subsequently, design of experiment (DoE) using the optimal Latin hypercube sampling (OLHS) method is carried out, considering the total mass and the torsional stiffness of the automobile body, the maximum intrusion deformation on the firewall, the maximum impact acceleration at lower end of the B-pillar, and the total material cost of the selected optimization parts as five competing optimization objectives. After that, the optimal combination of thickness and material parameters of the optimization parts is determined using EGRA and confirmed by technique for order preference by similarity to ideal solution (TOPSIS). Finally, a comparison between the original design and the post-lightweight design, namely the optimized design, further confirms the effectiveness of the lightweight optimization. According to the outcomes, the automobile body is lightweight optimized with a mass decrease of 4.98 kg on the basis of well guaranteeing other relevant mechanical performance. Accordingly, the EGRA could be well employed to the multi-objective lightweight optimization of the automobile body.