Prediction of Mechanical Properties of Fe 415 Steel in Hot Rolling Process Using Artificial Neural Network

Abstract

The purpose of the work is to predict the strength of the rolled product using artificial neural network (ANN). A network model is developed to predict the strength of rolled Fe 415 steel. Manual test result data are collected from testing laboratory for output/response parameters, viz. UTS, YS and % elongation by taking FST, WP, WFR and LHT as input parameters. Then, ANN tool is used to train the network using feedforward backpropagation method and validate the network for best fit. The network model is tested with 70% of data values, and probability of prediction is checked. Predicted value of parameters is compared with experimental values via percentage deviation and confirmative analysis. In the result, the predicted value of strength is found to be comparable and satisfactory with experimental values with deviation value as − 0.2250. The suggested ANN model can also be utilized for the prediction of properties of other processes.