Characterization and Optimization of Pearlite Microstructure Using Persistent Homology and Bayesian Optimization

Abstract

Pearlite spheroidization is substantially a spatial-geometric evolution of cementite. In this study, a persistent homology analysis was employed to characterize the topological features of cementite component of pearlite steel, through which the lamellar and spherical pearlite microstructures were successfully distinguished. Because the mechanical performance of pearlite steel is highly sensitive to the cementite configuration, an inverse conversion of persistent-homology digital data to an image for some properties of interest was proposed by using Bayesian optimization. The proposed microstructural optimization approach paves a way to interpret persistent-homology information in metallurgy and presents the feasibility of data-driven persistent-homology-based property predictions and microstructural optimization.