Data-driven phase recognition of steels for use in mechanical property prediction

Abstract

This paper develops a deep learning scheme of phase recognition for steel materials. A convolutional neural network classifier is established, such that the martensite phase, which has a substantial impact on the mechanical properties of steels, can be recognized from microstructure images and its volumetric fraction can also be estimated from multi-phase microconstituents. The testing results on an ultrahigh carbon steel dataset proved that the developed scheme has a rational phase recognition accuracy. The estimated martensite fraction can be used as an essential feature to predict the mechanical properties of materials in additive manufacturing.