Optimization of Initial Blank Shape with a Variable Blank Holder Force in Deep-Drawing via Sequential Approximate Optimization

Abstract

In this paper, the initial blank shape is optimized via sequential approximate optimization (SAO) with radial basis function (RBF) network. In deep drawing, the wrinkling and tearing are major defects. In addition, too small initial blank shape will results in the boundary of blank drawn into a trimming contour, which is the geometrical defect. Therefore, these three defects are taken as the design constraints. On the other hand, the flange area, which is often called the earing, is taken as the objective function. Also, variable blank holder force (VBHF) trajectory is determined. In this paper, new algorithm to evaluate the earing and the geometrical defect is also proposed for the SAO. Numerical result shows that the optimization of the initial blank shape with VBHF trajectory leads to defect-free product. In comparison with the use of square blank shape with a constant blank holder force, 60% reduction of the waste of material can be achieved.

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

“…For example, Zheng et al [3] and Su et al [4] studied the formability and performance of plates with different material properties. Modi et al [5] and Srirat et al [6] focused on the BHF to improve the forming quality. Lela et al [7] determined the mathematical model of the friction coefficient in deep drawing.…”