Hierarchical Multi-Objective Optimization of Automobile Seat Frame Based on Grey Fuzzy Logic System

Abstract

In development of the automobile industry, lightweight and safety performance are contradictory, yet they are of great significance.The optimization process of auto parts is a high-dimensional optimization problem which has a variety of regulations limits and safety tests at the same time. In order to address this issue, hierarchical multi-objective optimization of a passenger car seat frame is carried out in this research. Different from previous researches, in this paper, all car seat frame parts are listed as the optimization objects and are given different optimized attributes. Meanwhile, in order to achieve the goal of effectively reducing the sample sizes of the design of experiments, hierarchical optimization is proposed, and the optimization process is divided into three stages. In each stage, components with different optimized attributes are introduced into various safety tests and then conducting design of experiments. On the other hand, through adopting the grey fuzzy logic system to assign the appropriate optimized grade, the optimization process is simplified and the errors caused by the manual selection or unified optimization levels are avoided. The proposed method is considered to be an universal approach to solve the lightweight optimization of the auto parts. Design parameters of the car seat frame before and after the hierarchical multi-objective optimization are compared, it illustrated that total mass and material cost of the seat frame are reduced by 2.3kg (28.5%) and 13.8 (32.4%) respectively. Moreover, various comparisons are carried out to verify the validity of the optimization method proposed in this paper. In conclusion, the proposed method is quite promising yet with less sample points associated, is an effective mean to solve the multi-objective optimization problems of automobile component. INDEX TERMSAutomobile seat frame lightweight, hierarchical multi-objective optimization, adaptive design of experiments, grey fuzzy logic system.

Multi-objective optimization of an automobile seat frame based on collision condition and gray fuzzy decision

“…The original sequence can be normalized via equation (6), and because the normal range of the strain index is 0-1, it does not need to be normalized. In this study, six optimization-grade fuzzy languages of "-1," "0," "1," "2," "3," and "4" were defined based on engineering experience 9 . Given that the contribution of each part to the safety performance of the automobile seat skeleton varies, different parts were assigned different optimization grades, as shown in table 3.…”