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

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 direc...

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Vydané v:	Npj Materials degradation Ročník 5; číslo 1; s. 1 - 10
Hlavní autori:	Mamun, Osman, Wenzlick, Madison, Sathanur, Arun, Hawk, Jeffrey, Devanathan, Ram
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 16.04.2021 Nature Publishing Group Nature Portfolio
Predmet:	639/301/1023/1026 639/301/1023/303 Algorithms Austenitic stainless steels Chemistry and Materials Science Correlation coefficients Corrosion and Coatings Creep (materials) Datasets Electrochemistry Ferritic stainless steels Heat resistant alloys High temperature Machine learning MATERIALS SCIENCE mechanical properties metals and alloys Model testing Parameterization Rupture Structural Materials Tribology
ISSN:	2397-2106, 2397-2106
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