Integrated machine learning approach for predicting low-temperature embrittlement in duplex stainless steels via ferrite hardening

The low-temperature embrittlement of duplex stainless steels significantly restricts their broader application in critical environments. Despite extensive research, a reliable predictive model for this phenomenon remains unavailable. In this study, we present a data-driven framework that integrates two machine learning (ML) models to predict the evolution of ferrite hardness and steel toughness during thermal ageing. We assembled two experimental datasets from both the open literature and in-house experiments. The ferrite hardness dataset includes chemical composition and ageing conditions, while the steel toughness dataset incorporates all features from the ferrite dataset, along with ferrite grain size and ferrite fraction. A systematic selection of input features and ML algorithms was conducted to optimize model performance. The integrated framework is based on random forest regression, where ML1 predicts changes in ferrite hardness, and ML2 estimates variations in steel toughness using the predicted ferrite hardness from ML1 as an input feature. This linkage reflects the metallurgical understanding that ferrite hardness serves as a key indicator of low-temperature embrittlement in duplex stainless steel. The trained models achieved high predictive accuracy, with R 2 values exceeding 0.97 for both ferrite hardness and steel toughness across multiple stainless steel grades. Furthermore, the models demonstrated strong generalizability when applied to unseen alloys and new ageing conditions. To assess model interpretability, feature importance analysis was performed to evaluate the influence of individual input variables, and the results were interpreted through the lens of physical metallurgy, offering insights into the underlying mechanisms of embrittlement. • Integrated machine learning model low-temperature embrittlement in stainless steel • Achieves R 2 > 0.97 for ferrite hardness and steel toughness predictions • Framework generalizes well to unseen alloys and ageing conditions • Feature importance reveals key drivers of low-temperature embrittlement