An investigation of hybrid models FEA coupled with AHP-ELECTRE, RSM-GA, and ANN-GA into the process parameter optimization of high-quality deep-drawn cylindrical copper cups

Abstract

Deep drawing is one of the primary sheet metal forming processes that is used all over the world. The current study focused on using Analytical Hierarchy Process-Elimination and Choice Translating the Reality (AHP-ELECTRE I), Response Surface Methodology-Genetic Algorithm (RSM-GA), and Artificial Neural Network-Genetic Algorithm (ANN–GA) for determining deep–drawing performance parameters. A hybrid FEA–MCDA–RSM–ANN–GA was built using an experimental design obtained from RSM to develop better quality products. It is integrated with finite element-based numerical deep drawing simulation to understand the intended responses and the impact of design factors without the need for costly trial tests. To improve the quality of drawn cups characterization, three process parameters-clearance, punch radius, and coefficient of friction-have been tuned to their optimum values like resultant tool force (N), spring back (µm), max forming limit curve (%), and max thinning rate. The optimization results showed the efficacy of the technique for process design, resulting in the reduction of both cost and time. The desirability index was calculated and compared with all the predictions. The hybrid models that have been developed may be suggested for accurate prediction and optimization of various process parameters and outcomes for any industrial application issues that could arise.