Trustworthy Multimodal Attention Framework for Creep Rupture Life Prediction Under Data-Scarce Conditions: A Case Study on IN718.

An attention-based multimodal deep learning framework is developed to fuse processing parameters with microstructural micrographs for predicting creep rupture life of IN718 under a fixed creep testing condition. Under a predefined composition-stratified, sample-level split, the framework achieved a mean test-set R2 of 0.917 ± 0.014 across 50 random-seed training repetitions. The corresponding mean RMSE and MAPE were 0.14% and 6.0%, respectively. Interpretability analyses suggest that the predictions are consistent with established metallurgical understanding, particularly the important role of δ-phase characteristics. Furthermore, uncertainty quantification endows the model with self-assessment capabilities, allowing it to reliably quantify the confidence of its predictions. This study establishes a methodological framework that unifies predictive accuracy, physical interpretability, and model confidence, providing a validated paradigm for developing trustworthy AI models for materials design under data-limited conditions.