Computer simulation of microstructure transformation in heat treatment processes

Abstract

Purpose: Most often used methods for prediction of austenite decomposition are described and analysed. Design/methodology/approach: The austenite decomposition prediction is usually based on continuous cooling transformation (CCT) diagrams. The next method is based on semi-empirical approach based on the Scheil’ s additivity rule. The third method is based on time, t8/5, relevant for microstructure transformation measured on Jominy-specimen. Very good results are obtained by artificial neural network (ANN) with learning rule based on the error backpropagation algorithm. Findings: By the comparison of application ability of investigated methods in mathematical modelling and computer simulation of austenite decomposition during the cooling of low-alloyed steel, it can be concluded that everyone method gives different results, and minimum variation in chemical composition and history of cooling may produce extremely different results in microstructure portion. Very good results were achieved by the method, which applies the Jominy-test results. In this method the additivity rule and specific performance of Jominy-test has been combined. Research limitations/implications: The investigation was performed on low-alloyed steels. Practical implications: The results of prediction of microstructure transformations could be used for prediction of mechanical properties after a heat treatment and of generation of stresses and strains during a heat treatment. Originality/value: The ability of application of potential methods in prediction of austenite decomposition in mathematical modelling of heat treatment of steel is analysed. The finding of this paper will be so useful in development new algorithms in mathematical modelling and computer simulation of heat treatment of low-alloyed steels.