Predicting stress distribution in cold-formed material with genetic programming

In this paper we propose a genetic programming approach to predict radial stress distribution in cold-formed material. As an example, cylindrical specimens of copper alloy were forward extruded and analysed by the visioplasticity method. They were extruded with different coefficients of friction. The values of three independent variables (i.e., radial and axial position of measured stress node, and coefficient of friction) were collected after each extrusion. These variables influence the value of the dependent variable, i.e., radial stress. On the basis of training data set, various different prediction models for radial stress distribution were developed during simulated evolution. Accuracy of the best models was proved with the testing data set. The research showed that by proposed approach the precise prediction models can be developed; therefore, it is widely used also in other areas in metal-forming industry, where the experimental data on the process are known.