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

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

Shen, Chunguang (Northeastern University China)
Wang, Chenchong (Northeastern University China)
Wei, Xiaolu (Northeastern University China)
Li, Yong (Northeastern University China)
van der Zwaag, S. (TU Delft Novel Aerospace Materials)
Xu, W. (TU Delft Novel Aerospace Materials; Northeastern University China)

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 high-end 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.

Alloy design
Machine learning
Physical metallurgy
Small sample problem
Stainless steel

http://resolver.tudelft.nl/uuid:f1254cd4-f218-49a9-a854-115742be8de0

https://doi.org/10.1016/j.actamat.2019.08.033

2020-02-01

1359-6454

Acta Materialia, 179, 201-214

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2019 Chunguang Shen, Chenchong Wang, Xiaolu Wei, Yong Li, S. van der Zwaag, W. Xu