Machine Learning Unveils the Impacts of Key Elements and Their Interaction on the Ambient-Temperature Tensile Properties of Cast Titanium Aluminides Employing SHAP Analysis - Shiqiu Liu; Li Liang (Crystals)
This study facilitates the data-driven design of novel cast TiAl alloys by systematically investigating the critical elements and their interactions affecting room-temperature (RT) tensile properties by the machine learning method based on SHAP analysis. Comparative analysis of three algorithms within the training dataset proved the random forest regression (RFR) as the optimal modeling approach. To evaluate model performance and prevent overfitting, leave-one-out cross-validation (LOOCV) was simultaneously implemented during training. All the three well-trained models demonstrated robust predictive capabilities for ultimate tensile strength (UTS), elongation (EL), and yield strength (YS). Detailed investigation on both the magnitude and directionality of feature importance and interaction disclosed distinct elemental influences: B, C, and Nb predominantly improved UTS and YS, while Cr, Mn, and Al positively affected EL. The highly probable direction of feature interaction between two different elements on the RT tensile properties of cast TiAl alloys was basically revealed. Notably, Al–B interactions enhance UTS at Al < 45.5 at%; Cr–Mn synergistically improves EL when Cr > 1 at%; both Al–B and Al–C interactions boost YS within 44–46 at% Al. Despite a slight distinction in casting technology, this research established a qualitative relationship between the chemical elements and the RT tensile properties of TiAl alloys, providing design recommendations for cast TiAl alloys with excellent RT tensile properties.