Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys

Abstract Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to predict the oxidation rate constant of NiCr-based a...

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Autores principales:	Jian Peng, Rishi Pillai, Marie Romedenne, Bruce A. Pint, Govindarajan Muralidharan, J. Allen Haynes, Dongwon Shin
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/87f65d909fea40b68565da846e7fd0e6
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spelling	oai:doaj.org-article:87f65d909fea40b68565da846e7fd0e62021-12-02T16:28:05ZData analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys10.1038/s41529-021-00188-z2397-2106https://doaj.org/article/87f65d909fea40b68565da846e7fd0e62021-08-01T00:00:00Zhttps://doi.org/10.1038/s41529-021-00188-zhttps://doaj.org/toc/2397-2106Abstract Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to predict the oxidation rate constant of NiCr-based alloys as a function of composition and temperature with a highly consistent and well-curated experimental dataset. Two characteristic oxidation models, i.e., a simple parabolic law and a statistical cyclic oxidation model, have been chosen to numerically represent the high-temperature oxidation kinetics of commercial and model NiCr-based alloys. We have successfully trained machine learning (ML) models using highly ranked key input features identified by correlation analysis to accurately predict experimental parabolic rate constants (k p). This study demonstrates the potential of ML approaches to predict oxidation kinetics of alloys over wide composition and temperature ranges. This approach can also serve as a basis for introducing more physically meaningful ML input features to predict the comprehensive cyclic oxidation behavior of multi-component high-temperature alloys with proper constraints based on the known underlying mechanisms.Jian PengRishi PillaiMarie RomedenneBruce A. PintGovindarajan MuralidharanJ. Allen HaynesDongwon ShinNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492ENnpj Materials Degradation, Vol 5, Iss 1, Pp 1-8 (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 Jian Peng Rishi Pillai Marie Romedenne Bruce A. Pint Govindarajan Muralidharan J. Allen Haynes Dongwon Shin Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys
description	Abstract Although of practical importance, there is no established modeling framework to accurately predict high-temperature cyclic oxidation kinetics of multi-component alloys due to the inherent complexity. We present a data analytics approach to predict the oxidation rate constant of NiCr-based alloys as a function of composition and temperature with a highly consistent and well-curated experimental dataset. Two characteristic oxidation models, i.e., a simple parabolic law and a statistical cyclic oxidation model, have been chosen to numerically represent the high-temperature oxidation kinetics of commercial and model NiCr-based alloys. We have successfully trained machine learning (ML) models using highly ranked key input features identified by correlation analysis to accurately predict experimental parabolic rate constants (k p). This study demonstrates the potential of ML approaches to predict oxidation kinetics of alloys over wide composition and temperature ranges. This approach can also serve as a basis for introducing more physically meaningful ML input features to predict the comprehensive cyclic oxidation behavior of multi-component high-temperature alloys with proper constraints based on the known underlying mechanisms.
format	article
author	Jian Peng Rishi Pillai Marie Romedenne Bruce A. Pint Govindarajan Muralidharan J. Allen Haynes Dongwon Shin
author_facet	Jian Peng Rishi Pillai Marie Romedenne Bruce A. Pint Govindarajan Muralidharan J. Allen Haynes Dongwon Shin
author_sort	Jian Peng
title	Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys
title_short	Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys
title_full	Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys
title_fullStr	Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys
title_full_unstemmed	Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys
title_sort	data analytics approach to predict high-temperature cyclic oxidation kinetics of nicr-based alloys
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/87f65d909fea40b68565da846e7fd0e6
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Data analytics approach to predict high-temperature cyclic oxidation kinetics of NiCr-based Alloys

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