On the use of transfer modeling to design new steels with excellent rotating bending fatigue resistance even in the case of very small calibration datasets

In this research a machine learning model for predicting the rotating bending fatigue strength and the high-throughput design of fatigue resistant steels is proposed. In this transfer prediction framework, machine learning models are first trained to estimate tensile properties (yield strength, tensile strength and elongation) on the basis of...

Fundamentals and application of solid-state phase transformations for advanced high strength steels containing metastable retained austenite

Over many decades, significant efforts have been made to improve the strength-elongation product of advanced high strength steels (AHSSs) by creating tailored multi-phase microstructures. Successive solid-state phase transformations for steels with a well selected chemical composition turned out to be the key instrument in the realisation of...

Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel

With the development of the materials genome philosophy and data mining methodologies, machine learning (ML) has been widely applied for discovering new materials in various systems including high-end steels with improved performance. Although recently, some attempts have been made to incorporate physical features in the ML process, its...