High temperature oxidation of corrosion resistant alloys from machine learning

Abstract Parabolic rate constants, k p , were collected from published reports and calculated from corrosion product data (sample mass gain or corrosion product thickness) and tabulated for 75 alloys exposed to temperatures between ~800 and 2000 K (~500–1700 oC; 900–3000 oF). Data were collected for...

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Autores principales:	Christopher D. Taylor, Brett M. Tossey
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/819f126227c64cec9bc0a507cce9efe4
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spelling	oai:doaj.org-article:819f126227c64cec9bc0a507cce9efe42021-12-02T15:32:56ZHigh temperature oxidation of corrosion resistant alloys from machine learning10.1038/s41529-021-00184-32397-2106https://doaj.org/article/819f126227c64cec9bc0a507cce9efe42021-07-01T00:00:00Zhttps://doi.org/10.1038/s41529-021-00184-3https://doaj.org/toc/2397-2106Abstract Parabolic rate constants, k p , were collected from published reports and calculated from corrosion product data (sample mass gain or corrosion product thickness) and tabulated for 75 alloys exposed to temperatures between ~800 and 2000 K (~500–1700 oC; 900–3000 oF). Data were collected for environments including lab air, ambient and supercritical carbon dioxide, supercritical water, and steam. Materials studied include low- and high-Cr ferritic and austenitic steels, nickel superalloys, and aluminide materials. A combination of Arrhenius analysis, simple linear regression, supervised and unsupervised machine learning methods were used to investigate the relations between composition and oxidation kinetics. The supervised machine learning techniques produced the lowest mean standard errors. The most significant elements controlling oxidation kinetics were Ni, Cr, Al, and Fe, with Mo and Co composition also found to be significant features. The activation energies produced from the machine learning analysis were in the correct distributions for the diffusion constants for the oxide scales expected to dominate in each class.Christopher D. TaylorBrett M. TosseyNature 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 Christopher D. Taylor Brett M. Tossey High temperature oxidation of corrosion resistant alloys from machine learning
description	Abstract Parabolic rate constants, k p , were collected from published reports and calculated from corrosion product data (sample mass gain or corrosion product thickness) and tabulated for 75 alloys exposed to temperatures between ~800 and 2000 K (~500–1700 oC; 900–3000 oF). Data were collected for environments including lab air, ambient and supercritical carbon dioxide, supercritical water, and steam. Materials studied include low- and high-Cr ferritic and austenitic steels, nickel superalloys, and aluminide materials. A combination of Arrhenius analysis, simple linear regression, supervised and unsupervised machine learning methods were used to investigate the relations between composition and oxidation kinetics. The supervised machine learning techniques produced the lowest mean standard errors. The most significant elements controlling oxidation kinetics were Ni, Cr, Al, and Fe, with Mo and Co composition also found to be significant features. The activation energies produced from the machine learning analysis were in the correct distributions for the diffusion constants for the oxide scales expected to dominate in each class.
format	article
author	Christopher D. Taylor Brett M. Tossey
author_facet	Christopher D. Taylor Brett M. Tossey
author_sort	Christopher D. Taylor
title	High temperature oxidation of corrosion resistant alloys from machine learning
title_short	High temperature oxidation of corrosion resistant alloys from machine learning
title_full	High temperature oxidation of corrosion resistant alloys from machine learning
title_fullStr	High temperature oxidation of corrosion resistant alloys from machine learning
title_full_unstemmed	High temperature oxidation of corrosion resistant alloys from machine learning
title_sort	high temperature oxidation of corrosion resistant alloys from machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/819f126227c64cec9bc0a507cce9efe4
work_keys_str_mv	AT christopherdtaylor hightemperatureoxidationofcorrosionresistantalloysfrommachinelearning AT brettmtossey hightemperatureoxidationofcorrosionresistantalloysfrommachinelearning
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High temperature oxidation of corrosion resistant alloys from machine learning

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