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 highend steels with improved performance. Although recently, some attempts have been made to incorporate physical features in the ML process, its effects have not been demonstrated and systematically analysed nor experimentally validated with prototype alloys. To address this issue, a physical metallurgy (PM) -guided ML model was developed, wherein intermediate parameters were generated based on original inputs and PM principles, e.g., equilibrium volume fraction (V f ) and driving force (D f ) for precipitation, and these were added to the original dataset vectors as extra dimensions to participate in and guide the ML process. As a result, the ML process becomes more robust when dealing with small datasets by improving the data quality and enriching data information. Therefore, a new material design method is proposed combining PM-guided ML regression, ML classifier and a genetic algorithm (GA). The model was successfully applied to the design of advanced ultrahigh-strength stainless steels using only a small database extracted from the literature. The proposed prototype alloy with a leaner chemistry but better mechanical properties has been produced experimentally and an excellent agreement was obtained for the predicted optimal parameter settings and the final properties. In addition, the present work also clearly demonstrated that implementation of PM parameters can improve the design accuracy and efficiency by eliminating intermediate solutions not obeying PM principles in the ML process. Furthermore, various important factors influencing the generalizability of the ML model are discussed in detail.
Numerous high-performance steels with various compositions and mechanical properties were developed to enable a safe and light-weight automotive body-in-white (BIW). However, this multisteel scheme creates substantial challenges, including the resistance spot welding of dissimilar steels, processing optimization, and recycling. Here, we propose a revolutionary unified steel (UniSteel) concept, i.e., using a single chemistry to produce multiple steel grades for the entire BIW. The tensile strengths of various UniSteel grades are ranging from 600 to 1680 MPa, encompassing the strengths of typical commercial counterparts while exhibiting competent ductility. The prototype parts made of UniSteel press-hardened steel (PHS) grade demonstrate superior side-intrusion resistance over the commercial PHS, and the satisfactory weldability is verified. The UniSteel reduces the resistivity difference within the sheet stack-ups, allowing the simplification of welding processes. The UniSteel concept could potentially revolutionize the manufacturing of BIW for the global automotive industry and contribute to carbon neutrality.
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