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

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 und...

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Autores principales:	Osman Mamun, Madison Wenzlick, Jeffrey Hawk, Ram Devanathan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/094eb6936e194c509aa2d4fd340cdf77
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spelling	oai:doaj.org-article:094eb6936e194c509aa2d4fd340cdf772021-12-02T11:37:26ZA machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys10.1038/s41598-021-83694-z2045-2322https://doaj.org/article/094eb6936e194c509aa2d4fd340cdf772021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83694-zhttps://doaj.org/toc/2045-2322Abstract 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.Osman MamunMadison WenzlickJeffrey HawkRam DevanathanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Osman Mamun Madison Wenzlick Jeffrey Hawk Ram Devanathan A machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys
description	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.
format	article
author	Osman Mamun Madison Wenzlick Jeffrey Hawk Ram Devanathan
author_facet	Osman Mamun Madison Wenzlick Jeffrey Hawk Ram Devanathan
author_sort	Osman Mamun
title	A machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys
title_short	A machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys
title_full	A machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys
title_fullStr	A machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys
title_full_unstemmed	A machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys
title_sort	machine learning aided interpretable model for rupture strength prediction in fe-based martensitic and austenitic alloys
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/094eb6936e194c509aa2d4fd340cdf77
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A machine learning aided interpretable model for rupture strength prediction in Fe-based martensitic and austenitic alloys

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