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

Mamun, Osman; Wenzlick, Madison; Sathanur, Arun; Hawk, Jeffrey; Devanathan, Ram

doi:10.1038/s41529-021-00166-5

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

Journal Article · Fri Apr 16 00:00:00 EDT 2021 · npj Materials Degradation

DOI:https://doi.org/10.1038/s41529-021-00166-5· OSTI ID:1777650

; Wenzlick, Madison; Sathanur, Arun; Hawk, Jeffrey;

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.

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Research Organization:: National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)

Sponsoring Organization:: USDOE; USDOE Office of Fossil Energy (FE)

Grant/Contract Number:: 89243318CFE000003

OSTI ID:: 1777650

Alternate ID(s):: OSTI ID: 1810532
OSTI ID: 1843614

Journal Information:: npj Materials Degradation, Journal Name: npj Materials Degradation Journal Issue: 1 Vol. 5; ISSN 2397-2106

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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Machine Learning Augmented Predictive and Generative Model for Rupture Life in Ferritic and Austenitic Steels

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