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Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel
یادگیری ماشین با هدایت متالورژی فیزیکی و طراحی هوشمند مصنوعی از فولاد ضد زنگ قوی-2019 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 (Vf) and driving force (Df) 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. Keywords: Alloy design | Machine learning | Physical metallurgy | Small sample problem | Stainless steel |
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