Exploring the Possibilities of Development of Directly Quenched TRIP-Aided Steel by the Artificial Neural Networks (ANN) Technique

Abstract

In TRIP-aided steels, generally the composition-process combination is aimed at circumventing pearlitic transformation during cooling of austenite and to retain the desired volume fraction of austenite (∼10 vol%) in the microstructure, which is amenable to stress/strain induced transformation during deformation. The purpose is achieved by individual and interactive contribution of numbers of compositional and process variables. Therefore, it is impractical to predict the best combination of most significant variables by using conventional expertise. In this regard, the artificial neural network (ANN) technique has already been established as a potential tool for composition–process–properties correlation in various materials. In the present study, the ANN technique is utilized to predict the composition–process–properties correlation with an aim to achieve the most attractive strength–ductility combination in TRIP aided steel. In the course of the aforesaid exercise, it is indicated that an attractive strength–ductility combination may be achieved without much requirement of intercritical annealing (ICA) and isothermal holding at bainitic temperature, even at lower level of carbon (say, 0.1 wt%), with judicious alloying by Cu and Ni. The hypothesis is first tested by conducting dilatometric study and microstructural examination of the dilatometric samples and subsequently ascertained by determination of mirostructure and mechanical properties of the as hot roll samples of predicted compositions.

Keywords:

• Artificial neural networks (ANN)
• Direct quenching
• Intercritical annealing (ICA)
• Microstructure
• TRIP-aided steels