Digital steel heat treatment design integrating transformation kinetics microstructure architecture and prescriptive digital twins

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Authors: Md. Abdus Shabur
Cited by: 1

Abstract

Modern structural engineering necessitates a paradigm shift from conventional single-path heat treatment toward a comprehensive framework of microstructure architecture to simultaneously optimize strength, ductility, and dimensional stability. This review is based on the synthesis of the evidence presented by 97 peer-reviewed articles that were located by implementing specific search queries in authentic databases. The study concentrates on five formulated questions so as to close the gap existing between basic phase-transformation kinetics and industrial manufacturability. We are considering a wide range of novel paths such as bulk metastable austenite engineering through Quenching and Partitioning (Q&P), nanostructured bainite, and high-speed thermal processing, and manufacturing-integrated systems such as press hardening. The study answers these research questions and concludes that high-impact performance is primarily determined by the spatial topology and mechanical stability of retained austenite, in other words, film versus blocky morphologies, and not merely simple phase fractions. In addition, the review confirms that the strength of industries is constrained by microstructural variability concealed within a transient boundary condition and distortion due to quenching. To address these limitations, we examine how physics-based Thermo-Metallurgical-Mechanical (TMM) modeling can be converged to new data-driven surrogates and prescriptive so-called Digital Twins. This synthesis gives a generalizable process designing methodology to avoid uncertainty in the design of processes, where standard microstructure-property descriptors and closed-loop control are the keys to take advanced metallurgical ideas, which are proven in the laboratory, and translate them into dependable industrial manufacturing.

Fields of Study & Subjects

Additive Manufacturing Materials and Processes Microstructure and Mechanical Properties of Steels Machine Learning in Materials Science

Publication details

Journal / source
Discover Materials
Publisher
Springer Science and Business Media LLC
Publication date
27 June 2026
Language
EN