Neural Network-Based Multi-Objective Optimization of Adjustable Drawbead Movement for Deep Drawing of Tailor-Welded Blanks

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.

A novel approach to the conventional deep drawing process

“…They also improve the drawing quality in the mechanical deep drawing processes. 35,36 During the mechanical pre-bulging process, a decrease in force was observed due to the increase in the entrance angle, the easier flow of the sheet into the die cavity, and the prevention of the formation of wrinkles beforehand. It was observed that the forming forces during the forming process tended to decrease with an increase in the die entrance angle.…”

Structural and Behaviour Optimization of Tubular Structures Made of Tailor Welded Blanks by Applying Taguchi and Genetic Algorithms Methods

“…The forming limit curves determined in the experimental tests were based on the modified Marciniak test. The displacement was determined by image analysis (stochastic network) [17,18].…”