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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.

Self-piercing riveting (SPR) is a high-speed fastening process that can join similar and dissimilar sheet materials without the need for pre-processing such as drilling or punching. During SPR processes, two overlapping sheets are joined by a rivet. The upper sheet is punched first by the rivet and then the lower sheet is deformed between the rivet and the die, creating a mechanical interlock. In this study, self-piercing riveting of aluminum alloy and carbon fiber reinforced polymer composites (CFRP) sheets was analysed using finite element simulations. For the finite element simulation of SPR processes, the orthogonal elasticity, the fracture model, and the cohesive zone model were used for describing the behaviour of CFRP. For validation of the composite material model, the punching process of CFRP was performed and the results were compared with FE predictions. The SPR process of the aluminum alloy and CFRP was simulated numerically and the performance of the joint was evaluated.

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