Digital identification scheme for steel microstructures in low-carbon steel

Abstract

This study proposes a scheme for digital identification of a steel microstructure based on crystallographic features. A 2D space for classifying mixed microstructures of bainite and martensite formed by seven different thermal cycles is defined using two dimensions related to variants in Kurdjumov–Sachs orientation relationships and dislocations. As a dimension relating to variants, the minor variant pair characterized by a rotation axis of 〈011〉 and a rotation angle of 52° is most useful in classifying the steel microstructure. Furthermore, the kernel average misorientation also served as a suitable dimension for the 2D space. The reasons for using the minor variant pair to classify steel microstructures are discussed from the viewpoint of phase transformation phenomena. The present study demonstrates the possibility for steel microstructures to be digitally identified.