A machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys

Autor: Osman Mamun, Madison Wenzlick, Jeffrey Hawk, Ram Devanathan
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Scientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-021-83694-z
Popis: Abstract The class of 9–12% Cr ferritic-martensitic alloys (FMA) and austenitic stainless steels have received considerable attention due to their numerous applications in high temperature power generation industries. To design high strength steels with prolonged service life requires a thorough understanding of the long-term properties, e.g., creep rupture strength, rupture life, etc., as a function of the chemical composition and processing parameters that govern the microstructural characteristics. In this article, the creep rupture strength of both 9–12% Cr FMA and austenitic stainless steel has been parameterized using curated experimental datasets with a gradient boosting machine. The trained model has been cross validated against unseen test data and achieved high predictive performance in terms of correlation coefficient ( $$R^{2} > 0.98 $$ R 2 > 0.98 for 9–12% Cr FMA and $$R^{2} > 0.95 $$ R 2 > 0.95 for austenitic stainless steel) thus bypassing the need for additional comprehensive tensile test campaigns or physical theoretical calculations. Furthermore, the feature importance has been computed using the Shapley value analysis to understand the complex interplay of different features.
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