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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Bibliographic Details
Published in:Npj Materials degradation Vol. 5; no. 1; pp. 1 - 10
Main Authors: Mamun, Osman, Wenzlick, Madison, Sathanur, Arun, Hawk, Jeffrey, Devanathan, Ram
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 16.04.2021
Nature Publishing Group
Nature Portfolio
Subjects:
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
Online Access:Get full text
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Summary: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.
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USDOE Office of Fossil Energy (FE)
89243318CFE000003
ISSN:2397-2106
2397-2106
DOI:10.1038/s41529-021-00166-5