Multi-Objective Optimization of VBHF in Deep Drawing Based on the Improved QO-Jaya Algorithm

Abstract

Blank holder force (BHF) is a crucial parameter in deep drawing, having close relation with the forming quality of sheet metal. However, there are different BHFs maintaining the best forming effect in different stages of deep drawing. The variable blank holder force (VBHF) varying with the drawing stage can overcome this problem at an extent. The optimization of VBHF is to determine the optimal BHF in every deep drawing stage. In this paper, a new heuristic optimization algorithm named Jaya is introduced to solve the optimization efficiently. An improved “Quasi-oppositional” strategy is added to Jaya algorithm for improving population diversity. Meanwhile, an innovated stop criterion is added for better convergence. Firstly, the quality evaluation criteria for wrinkling and tearing are built. Secondly, the Kriging models are developed to approximate and quantify the relation between VBHF and forming defects under random sampling. Finally, the optimization models are established and solved by the improved QO-Jaya algorithm. A VBHF optimization example of component with complicated shape and thin wall is studied to prove the effectiveness of the improved Jaya algorithm. The optimization results are compared with that obtained by other algorithms based on the TOPSIS method.

Numerical Optimization of Variable Blank Holder Force Trajectories in Stamping Process for Multi-Defect Reduction

“…However, some more practical and in-depth conclusions need to be drawn, instead of just a result parameter, to help engineering adjust process parameters in specific cases. Researchers, including Feng et al [ 21 , 22 ], Zhai et al [ 23 ], Xie et al [ 24 ], Taşkın et al [ 25 ], Jiang et al [ 26 ], Yu et al [ 27 ], and Guo et al [ 10 ], have enriched this field by merging an area of design techniques incorporating the latest developments. Implementing these advanced computational techniques in the context of sheet metal forming represents a convergence of machine learning, optimization algorithms, and computational physics, but promising substantial addresses of the complex, nonlinear, and stochastic nature of the metal forming process need more substantial results and discussion.…”