The Use of Acoustic Emission and Neural Network in the Study of Phase Transformation below M(S)

Acoustic emission and dilatometry were applied to investigate the characteristics of phase transformations in bearing steel 100CrMnSi6-4 during austempering below the martensite start temperature (M(S) 175 °C) at 150 °C. The aim of this study is to characterize the product of transformation occurring below the M(S) temperature using various research methods. Analysis of the dilatometric curves shows that, after the formation of athermal martensite below the M(S) temperature, the austenite continues to undergo isothermal transformation, indicating the formation of bainite. Additionally, tests were carried out with the use of acoustic emission during isothermal hardening of the adopted steel. The obtained acoustic emission signals were analyzed using an artificial neural network. The results, in the form of a graph of the frequency of acoustic emission (AE) event occurrence as a function of time, make it possible to infer about the bainite isothermal transformation. The results of this research may be used in the future to design optimal heat treatment methods and, consequently, may enable desired microstructure shaping.