Abstract
To improve the formability in the deep drawing of tailor-welded blanks, an adjustable drawbead was introduced. Drawbead movement was obtained using the multi-objective optimization of the conflicting objective functions of the fracture and centerline deviation simultaneously. Finite element simulations of the deep drawing processes were conducted to generate observations for optimization. The response surface method and artificial neural network were used to determine the relationship between variables and objective functions; the procedure was applied to a circular cup drawing of the tailor-welded dual-phase steel blank. The results showed that the artificial neural network had better prediction capability and accuracy than the response surface method. Additionally, the non-dominated sorting-based genetic algorithm (NSGA-II) could effectively determine the optima. The adjustable drawbead with the optimized movement was confirmed as an efficient and effective solution for improving the formability of the deep drawing of tailor-welded blanks.
Highlights
Sheet metal forming is an important production method in various industries
Considering the design priorities, every point can be selected as an optimum in each front; the artificial neural network (ANN) shows better accuracy than response surface model (RSM)
The results show that the proposed approach using ANN yielded the best results for both objectives; the ANN results were much more effective and accurate than the RSM
Summary
Sheet metal forming is an important production method in various industries. In this process, a sheet blank is transformed into a product with a complex shape and desirable engineering properties using tools. Tailor-welded blanks (TWBs) consist of two or more sheets whose material, thickness, and surface coating can be similar or different and are joined before the forming process. TWBs can reduce product weight, material consumption, and costs while improving strength [1]. With TWB sheets, understanding their formability has become essential for producing high-quality products in forming processes. Many studies have been conducted on TWB forming. TWBs were first used to overcome design challenges with existing materials, such as the Audi 100 floor panel [2,3]
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