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 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.

Automated summary

In this study, a direct rupture life parameterization using a gradient boosting algorithm was shown to effectively train machine learning models for highly accurate prediction of rupture life in various alloys. The Shapley value was utilized to quantify feature importance, making the model interpretable by identifying the impact of different features on performance. Additionally, a generative model based on a variational autoencoder was used to sample hypothetical synthetic candidate alloys from the learned joint distribution.