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Meeting

MS&T22: Materials Science & Technology

Symposium

AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis

Presentation Title

Machine Learning Guided Prediction of Rupture Time of 347H Stainless Steel

Author(s)

Mohammad Fuad Nur Taufique, Madison Wenzlick, Arun Sathanur, William Frazier, Ram Devanathan, Keerti Kappagantula, Shoieb Ahmed Chowdhury

On-Site Speaker (Planned)

Shoieb Ahmed Chowdhury

Abstract Scope

The creep resistance of 347H stainless steel depends on alloying elements, microstructures, and loading conditions. Therefore, it is important to predict the creep resistance i.e. rupture time of a 347H stainless steel before implementing an engineering design. Usually, semi-empirical time-temperature relations such as the Larson–Miller parameter (LMP) and the Manson–Haferd parameter (MHP) are used to predict the rupture time. However, these methods depend on empirical constants and often provide an unsatisfactory estimation for rupture lifetime for short to medium-term creep testing. In this study, we propose machine learning (ML) based calculations to accurately predict the rupture time of 347H stainless steels and highlight the importance of a high-quality dataset on the model performance. We achieved a coefficient of determination (R2) value of 0.88 from the gradient boosting regressor, which indicates a reliable model to predict the rupture time even though the data size is limited.

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