A multiscale data-driven framework for mechanical property prediction in LPBF-processed TA15 alloy: Integrating explainable machine learning with data augmentation

Key Points

• Mechanical properties of TA15 alloy are predicted through a novel approach that enhances accuracy.
• Key metrics show the model significantly improves predictions compared to traditional methods.
• Data-driven modeling employs explainable machine learning techniques, incorporating data augmentation for robustness.
• The findings suggest improved predictive capabilities for LPBF-processed materials, highlighting future applications.