Earing Reduction by Varying Blank Holding Force in Deep Drawing with Deep Neural Network

In the present study, we propose a novel method of varying blank holding force (BHF) with the segmental blank holder and investigated its influence on the earing reduction in the circular deep drawing process of an aluminum alloy sheet. Based on the analysis of cup height profile, the principle of varying BHF using segmental blank holder was presented and analyzed by analytical theory and numerical simulation. The optimal varying BHF was reasonably determined and compared by using the analytical model and deep neural network (DNN) model integrated with genetic algorithm (GA). The integrated DNN-GA model revealed an accurate prediction and optimization of varying BHF for the minimum earing height variation, which showed a superior result to the analytical model. The optimal varying BHF exhibited a significant influence on the earing formation, resulting in the noticeable decrease of earing height variation. For volume consistency, it was found that an increase in thickness at the cup wall region predicted with the optimal varying BHF was achieved in the transverse direction, which implies an improvement of deep-drawability. Such results indicate that the varying BHF is more reasonable and effective than the uniform BHF. Furthermore, the material properties of the blank sheet also affected the reduction of earing in the deep drawing with varying BHF. The present study revealed that the lower the material strength, the more significant the earing reduction in the deep drawing with varying BHF will be.