Data-driven predictions of retained austenite content and yield strength and elucidation of the sliding wear performance of carbide-free bainitic steels

Abstract

Carbide-free bainitic (CFB) steels have drawn much attention due to their high strength and toughness. The present work aims to build machine learning (ML) based surrogate models to predict retained austenite (RA) content and yield strength (YS) of isothermal transformed CFB steels and study the sliding wear performance. The input data for ML prediction were related to compositions, heat treatment process, and phases. The Random Forest regression and Gaussian process regression were respectively selected to build the predictive models for RA content and YS. Result shows that the predicted RA content agrees well with the experimentally determined ones in steel with high stability of untransformed austenite. For YS prediction, the present surrogate model can predict YS of CFB steels even without additional microstructural information such as dislocation and effective substructure size. Steels with different YS and RA content were designed to study the sliding wear performance. Result shows that the dominant wear mechanism of the tested steels is abrasive wear and the weight loss is inversely proportional to hardness when the load and wear distance are constant. Increasing YS and RA content can increase the initial hardness and work hardening ability at the worn surface, thereby enhancing wear resistance.