Modeling and multiobjective optimization of twist extrusion process

In this paper, the multi-objective optimization of twist extrusion process is carried out using the artificial neural network model and the genetic algorithm. the target purpose functions are equivalent plastic strain, strain distribution and extrusion force. the design variables are twist angle, friction factor and the loading rate. the FEM model of the process is first created and used to create training cases for the ANN, and the well-trained ANN is used as a quick and exact model of the process. Then the optimization of the design variables is conducted by an integrated genetic algorithm and the ANN model to create a set optimal solutions (pareto front).