Abstract
A novel and efficient data mining method for the segmentation of microstructural images of low-carbon steel is presented. Microstructural characterization has been the focus of many works in the field of materials science because microstructure is the fundamental element in understanding the link between process and property. Recently, deep-learning-based methods have been actively employed for microstructural classification since it has shown outstanding performance for solving image classification problems. However, previous applications of deep learning models to microstructural classification revealed limitations in that not only do they require the time-consuming labeling process but it is also still difficult to obtain a satisfactory result, especially for steel microstructures containing substances developed by displacive transformation. In this study, we propose a rulebased segmentation method that not only works without labeled images but also requires no prior knowledge of the number of microstructural constituents in each image. This unsupervised inference algorithm captures the morphological features of each microstructure and automatically finds the optimal number of microstructures having similar characteristics using a Bayesian Gaussian mixture model. The viability of our method is demonstrated by qualitative and quantitative evaluations with optical microscopy images of steel composed of different microstructures taken under different imaging conditions.
NSTL
Elsevier