Simulating Time Temperature Transformation Diagram of Steel Using Artificial Neural Network

Abstract

Design and development of steel is essentially governed by the Time-Temperature-Transformation (TTT) diagram. The diagram predicts the phase evolution during isothermal transformation schedules for a given chemistry. Selection of chemistry for obtaining a desired microstructure in steel under isothermal schedule needs determination of the TTT diagrams either by extensive experimental exercise or by rigorous thermodynamic calculations. Artificial neural network (ANN) technique has recently been employed as a versatile tool to predict the CCT diagrams of steels. The present work aims to identify a favorable composition capable of yielding an ultrafine bainitic microstructure by isothermal holding of austenite at low homologous temperature. To achieve this, TTT diagrams of varying compositions have been predicted a priori to reduce the required experimental trials. The exercise has led to the development of bainitic microstructure of nanoscale dimension in steel having 0.7C-2.0Mn-1.5Si-0.3Mo-1.5Cr (wt%). Experimental trial with the predicted composition of bainitic steel resulted into attractive combination of strength and ductility.