Hybrid optimisation of input process parameters of deep-drawn cylindrical cups from directional rolled copper strips

Abstract

Deep drawing is a method of producing sheet metal. It is extensively used in the automobile, packaging and home appliance industries. Incorrect parameter selection might induce defects in stamped components. Trial and error can improve product accuracy, but at the expense of execution and calculation. An optimisation approach and a response surface model are used to determine the optimal parametric parameter design. This study proposes a full and efficient optimisation approach, starting with modelling by RSM (Response Surface Methodology) and concluding with optimal process parameter identification. Based on this, a neural network was developed to improve cylindrical cup deep drawing. Three characteristics of fabricating the cups are clearance, punch radius and coefficient of friction. This parameter demonstrates the resultant tool force, spring back, maximum forming limiting curve and maximum thinning rate of cylinder cups. Initially, the three inputs are varied to measure the outcome using simulation software by Altair inspire form. Then, the equations for each output are designed using quadratic-based linear model fitting. The generated objective function is then used to establish the ideal process parameters for the cylindrical cups. The proposed method is compared to real-time physical experiments. The optimised parameter microstructure at the high-stress zone and the product quality are satisfactory without defects.