Multi-response optimization design of tailor-welded blank (TWB) thin-walled structures using Taguchi-based gray relational analysis

In order to further improve crashworthiness and reduce weight, tailor-welded blanks (TWBs) have been widely applied in auto-body design. In this paper, the discrete optimization design of TWBs structures with top-hat thin-walled section subjected to front dynamic impact is performed by using Taguchi-based gray relational analysis. Material grades and thicknesses with three levels are taken as discrete design variables. The total energy absorption (EA), the total weight (Mass) and the peak crashing force (Fmax) are chosen as optimization indicators. Considering the uncertain weight ratio of responses, four different cases would be analyzed. In order to determine the optimal parameter combination more accurately and eliminate errors from range analysis, the analysis of variance (ANOVA) would be performed. The optimized results demonstrate that it is feasible to increase the crashworthiness of TWBs by increasing the gray correlation of the structure. Compared to initial structure, case 1 (w(Fmax):w(EA):w(Mass)= 1/3:1/3:1/3) has the largest improvement among the four cases, i.e., the Fmax and the Mass are reduced by 29.3% and 2.7%, respectively, while the EA is increased by 3.5%. The discrete optimization method with only 27 iterations is a low computing cost or cost-effective and provides some guidance for some similar structural design. More comprehensive studies are essential to optimize performance of multi-components with more discrete variables.