Property prediction and properties-to-microstructure inverse analysis of steels by a machine-learning approach

Abstract

The design of new materials with useful properties is becoming increasingly important. Machine-learning tools Materials Genome Integration System Phase and Property Analysis (MIPHA) and rMIPHA (based on the R programming environment) have been independently developed to accelerate the process of materials discovery via a data-driven materials research approach. In the present work, MIPHA and rMIPHA are applied to steel, where machine-learning-based 2D/3D microstructural analysis, direct analysis of property predictions, and properties-to-microstructure inverse analysis were conducted. The results demonstrate that the prediction models deliver satisfactory performance. The inverse exploration of microstructures related to desired target properties (e.g., stress–strain curve, tensile strength, and total elongation) was realized. MIPHA and rMIPHA are still under improvement. The microstructure-to-processing inverse analysis is expected to be realized in the future.