Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels

Abstract The Larson–Miller parameter (LMP) offers an efficient and fast scheme to estimate the creep rupture life of alloy materials for high-temperature applications; however, poor generalizability and dependence on the constant C often result in sub-optimal performance. In this work, we show that...

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Autores principales:	Osman Mamun, Madison Wenzlick, Arun Sathanur, Jeffrey Hawk, Ram Devanathan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492
Acceso en línea:	https://doaj.org/article/e76d63eb1627409782b344abe8ba2147
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spelling	oai:doaj.org-article:e76d63eb1627409782b344abe8ba21472021-12-02T14:25:02ZMachine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels10.1038/s41529-021-00166-52397-2106https://doaj.org/article/e76d63eb1627409782b344abe8ba21472021-04-01T00:00:00Zhttps://doi.org/10.1038/s41529-021-00166-5https://doaj.org/toc/2397-2106Abstract The Larson–Miller parameter (LMP) offers an efficient and fast scheme to estimate the creep rupture life of alloy materials for high-temperature applications; however, poor generalizability and dependence on the constant C often result in sub-optimal performance. In this work, we show that the direct rupture life parameterization without intermediate LMP parameterization, using a gradient boosting algorithm, can be used to train ML models for very accurate prediction of rupture life in a variety of alloys (Pearson correlation coefficient >0.9 for 9–12% Cr and >0.8 for austenitic stainless steels). In addition, the Shapley value was used to quantify feature importance, making the model interpretable by identifying the effect of various features on the model performance. Finally, a variational autoencoder-based generative model was built by conditioning on the experimental dataset to sample hypothetical synthetic candidate alloys from the learnt joint distribution not existing in both 9–12% Cr ferritic–martensitic alloys and austenitic stainless steel datasets.Osman MamunMadison WenzlickArun SathanurJeffrey HawkRam DevanathanNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492ENnpj Materials Degradation, Vol 5, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Osman Mamun Madison Wenzlick Arun Sathanur Jeffrey Hawk Ram Devanathan Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels
description	Abstract The Larson–Miller parameter (LMP) offers an efficient and fast scheme to estimate the creep rupture life of alloy materials for high-temperature applications; however, poor generalizability and dependence on the constant C often result in sub-optimal performance. In this work, we show that the direct rupture life parameterization without intermediate LMP parameterization, using a gradient boosting algorithm, can be used to train ML models for very accurate prediction of rupture life in a variety of alloys (Pearson correlation coefficient >0.9 for 9–12% Cr and >0.8 for austenitic stainless steels). In addition, the Shapley value was used to quantify feature importance, making the model interpretable by identifying the effect of various features on the model performance. Finally, a variational autoencoder-based generative model was built by conditioning on the experimental dataset to sample hypothetical synthetic candidate alloys from the learnt joint distribution not existing in both 9–12% Cr ferritic–martensitic alloys and austenitic stainless steel datasets.
format	article
author	Osman Mamun Madison Wenzlick Arun Sathanur Jeffrey Hawk Ram Devanathan
author_facet	Osman Mamun Madison Wenzlick Arun Sathanur Jeffrey Hawk Ram Devanathan
author_sort	Osman Mamun
title	Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels
title_short	Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels
title_full	Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels
title_fullStr	Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels
title_full_unstemmed	Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels
title_sort	machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e76d63eb1627409782b344abe8ba2147
work_keys_str_mv	AT osmanmamun machinelearningaugmentedpredictiveandgenerativemodelforrupturelifeinferriticandausteniticsteels AT madisonwenzlick machinelearningaugmentedpredictiveandgenerativemodelforrupturelifeinferriticandausteniticsteels AT arunsathanur machinelearningaugmentedpredictiveandgenerativemodelforrupturelifeinferriticandausteniticsteels AT jeffreyhawk machinelearningaugmentedpredictiveandgenerativemodelforrupturelifeinferriticandausteniticsteels AT ramdevanathan machinelearningaugmentedpredictiveandgenerativemodelforrupturelifeinferriticandausteniticsteels
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Machine learning augmented predictive and generative model for rupture life in ferritic and austenitic steels

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