Void closure prediction in cold rolling using finite element analysis and neural network

Abstract

Cold rolling is used to eliminate void defects in cast materials thus improving the material performance during service. A comprehensive procedure is developed using finite element analysis and neural network to predict the degree of void closure. A three-dimensional nonlinear dynamic finite element model was used to study the mechanism of void deformation. Experiments were conducted to investigate void closure during the cold flat rolling process. Experimental results are compared to the three-dimensional finite element predictions to validate the model. The void reduction predictions from finite element analysis are in good agreement with experimental findings. Plastic strain, principal stress distribution around the void and void reduction ratio are presented for various case studies. As finite element simulation is time-consuming, a back-propagation neural network model is also developed to predict void closure behavior. Based on the correlation analysis, the reduction in sheet thickness, the dimension of the void and the size of the rollers were selected as the inputs for the neural network. The neural network model was trained based on results obtained from finite element analysis for various simulation cases. The trained neural network model provides an accurate and efficient procedure to predict void closure behavior in cold rolling.