Two-stage surrogate assisted differential evolution for optimization of a non-circular drawing sequence

Abstract

In this work, a two-stage surrogate assisted optimization technique is proposed for optimizing a non-circular drawing (NCD) sequence. The objective function was introduced to minimize inhomogeneity of the effective strain distribution at the cross-sections of the drawn wire which could deteriorate its delamination characteristics. The design variables introduced were die geometry and reduction of areas of the NCD sequence. Combination of Kriging (KG) models and support vector regression (SVR) which found to be an efficient technique in the literature is used as a surrogate model while a new circular sampling generation technique is proposed and applied leading to a two-stage surrogate assisted optimization strategy. The results revealed that using the proposed two-stage surrogate assisted optimization strategy can reduce surrogate model uncertainty within the same total number of training points compared to a traditional approach. The optimum results showed better effective strain homogeneity at the cross-section of the drawn wire than the original wire drawing (WD) process and NCD sequence with the same total reduction of area and less number of passes.