Multi-Objective Genetic Algorithm to Optimize Variable Drawbead Geometry for Tailor Welded Blanks Made of Dissimilar Steels

Formability of a tailor welded blank (TWB) is affected by the strength ratio of the base metals joined. In this paper, formability of TWB with very high strength ratio made by joining twinning‐induced plasticity (TWIP) and low carbon steels is numerically studied using a limiting dome height test. The drawbead geometry at the weaker side is modified to increase the dome height. The design of drawbead is optimized by treating it as a multi‐objective problem with maximum dome height and minimum weldline movement as objectives, which were constructed as metamodels through a genetic algorithms based approach. The necessary data for the metamodeling are generated by finite element (FE) simulation using the commercial solver, LS‐DYNA®. The multi‐objective optimization is carried out using a predator–prey genetic algorithm. The Pareto front estimated using this evolutionary approach is validated using FE simulations and a good correlation is obtained. Tailor welded blanks made by joining twinning‐induced plasticity (TWIP) and low carbon steel are studied using a multi‐objective genetic algorithm. The results of finite element simulations on them are captured in metamodels of weld line movement and formability by using the recently developed Evolutionary Neural Network (EvoNN) algorithm. Both are then simultaneously optimized to obtain a Pareto front, leading to some most preferable forming conditions.