Multi-objective optimization framework for five-pass wire-drawing process

A systematic method is proposed for optimizing the die geometry and processing conditions of a multi-pass wire-drawing process. In the proposed approach, FEM simulations based on the robust Taguchi design method are first performed to determine the drawing force, maximum surface axial stress, and maximum die stress for given values of the reduction angle, bearing length, and back tension properties of the die. The Taguchi analysis results are then used to train a neural network (NN) to predict the wire-drawing outcome for any given values of the input parameters. Finally, a genetic algorithm (GA) based on the NN is employed to determine the optimal settings of the input parameters. The validity of the GA optimization results is confirmed by means of FEM simulations. A simple method is proposed for visualizing the optimization results in 3-D space. The feasibility of the proposed framework is demonstrated by means of a practical case study involving a five-pass wire-drawing process for AISI 1022 low carbon steel.