Machine learning-driven predictive modeling of temperature-dependent mechanical properties in austenitic stainless steels

This work demonstrates that modern tree‑based models can effectively model complex, temperature-dependent mechanical responses, including highly nonlinear and even non-monotonic trends, in austenitic stainless steel and highlights limitations of composition‑only empirical models. To ensure robust model evaluation, we employed multiple validation strategies including repeated random train and test partitions and leave-one-out cross-validation. While one might assume that steel grade is fully captured by its composition, local assessments within narrower compositional ranges reveal different feature importance rankings than those observed in the full dataset. Grade-specific (AISI 304, 316, 321 and 347) feature importance analysis offered deeper insights into local alloy behavior and demonstrated the advantage of disaggregated modeling in avoiding misleading conclusions. Clustering and SHAP analyses further revealed a temperature-sensitive role of nitrogen, which strengthens the alloy through interstitial and fine precipitate mechanisms at lower temperatures but loses effectiveness at elevated temperatures due to precipitate coarsening. This highlights how data-driven methods can uncover metallurgically consistent, temperature-dependent strengthening behaviors not captured by simpler models. Our results confirm that temperature governs the mechanical performance of austenitic stainless steels, with other features contributing marginally, particularly for UTS. Additionally, the model achieved a notably high score for elongation, highlighting the critical role of testing temperature in addressing the long-standing challenge of poor elongation predictions in composition-only or composition-processing models. This suggests that low accuracy in previous studies is more likely due to dataset limitations rather than shortcomings of tree-based models.