Genetic algorithm (GA)–backpropagation (BP) network approach for hardness prediction of austempered ductile iron (ADI)

Abstract

Hardness serves as a crucial indicator for assessing the success of quenching treatment in the steel and iron industry, impacting the processability and wear properties of materials. In the present study, a dataset comprising 125 hardness values of the QT500-7 sample subjected to various austempering heat treatment parameters was utilised to train a neural network model for predicting the hardness of austempered ductile iron (ADI). The established model based on a genetic algorithm and error backpropagation algorithm demonstrates high accuracy in predicting the hardness of ADI if given heat treatment parameters. The mean square error of the model was about 1.019, indicating the reliability and precision of the model in predicting the hardness of ADI based on the specified heat treatment parameters.