Correlation between processing parameters and strain-induced martensitic transformation in cold worked AISI 301 stainless steel

Abstract

In this paper, the effect of cold-work temperature, the amount of deformation, the strain rate and the initial austenite grain size on the volume fraction of strain-induced martensite in AISI 301 stainless steel alloy was modeled by means of Artificial Neural Networks (ANNs). The optimal ANN architecture and training algorithm were determined. The results of the ANN model were in good agreement with experimental data taken from the literature. The appropriate range of processing parameters for grain refining through the Strain-Induced Martensitic Transformation and its Reversion to austenite process (SIMTR) was determined from this model.