Numerical multi-objective optimization of segmented and variable blank holder force trajectories in deep drawing based on DNN-GA-MCS strategy

In sheet metal forming, persistent challenges such as failure, wrinkling, and springback necessitate innovative solutions. This study elucidates a novel methodology tailored to mitigate these prevalent defects. Our primary aim was to bolster formability while concurrently diminishing defects throughout the forming cycle. To realize this objective, we deployed a segmented and variable blank holder force (S-VBHF) trajectory, facilitating precise adjustments to the blank holder force (BHF). In our quest to optimize process parameters, encompassing the S-VBHF, friction coefficient, and drawbead restraining force (DBRF), we amalgamated deep neural network, genetic algorithm, and Monte Carlo simulation techniques, collectively denoted as DNN-GA-MCS. The forming limit diagram (FLD) served as our rigorous evaluative framework, enabling a comprehensive assessment of sheet failure dynamics during the forming phase. Our proposed methodology underwent stringent validation via numerical simulations, with the cylindrical cup from NUMISHEET 2011 (BM1) providing the benchmark. Empirical outcomes underscored substantial enhancements in formed sheet quality: marked reductions of 8.33% in failure, 10.81% in wrinkling, and 5.88% in springback. In summation, our advanced methodology manifests potential in refining sheet metal forming processes, emphasizing its efficacy in substantially curtailing defects.