An intelligent system for predicting roll pressure in the cold rolling of Ti6Al4V

Abstract

Cold rolling is the most widely used metal forming process in which the metal sheet is passed in between a set of rolls. Roll pressure is the major parameter that influences thickness reduction. In the present work, explicit dynamic analysis was performed using Finite Element (FE) models to simulate the cold rolling process. Process parameters such as speed of the rolls, roller diameter, friction coefficient, and thickness reduction were considered for the investigation. Results from the FE simulation were used for training the artificial neural network (ANN) to estimate the roll pressure acted on the billet. The ANN model was developed in MATLAB and the numbers of neurons, as well as training and transfer functions of an artificial neural network, are varied for analysing the results. An optimal network is developed for predicting the roll pressure. The developed network is capable of considering the effect of main parameters such as roll speed, roll diameter, inlet thickness and outlet thickness of plate, and coefficient of friction. The developed network could successfully predict the roll pressure and the values are in close agreement with FE simulation. The developed ANN model is capable of predicting the roll pressure in different cold rolling conditions accurately.