A Machine Learning Method Based on TPE-XGBoost Model for TRIP/TWIP Near-Β Titanium Alloy Design

The traditional design method for TRIP/TWIP near-β titanium alloys is time-consuming and expensive. This paper proposes a machine-learning design method for a near-β titanium alloy that uses the Tree-structured Parzen Estimator (TPE) algorithm to optimize the hyperparameter space of XGBoost for improved model prediction accuracy in TRIP/TWIP. The model predicts the mechanical properties and plasticity mechanisms of the alloy based on its composition, key empirical parameters derived from the composition, and solution treatment temperature. The test results of the TPE-XGBoost model showed that when predicting the ultimate tensile strength (UTS), the adjusted determination coefficient (Adjusted R2), root mean square error (RMSE), and mean absolute error (MAE) values were 0.9264, 39.63MPa and 44.83MPa, respectively. When predicting elongation (El), the Adjusted R2, RMSE, and MAE values are 0.9134, 1.97%, and 1.89%, respectively. When predicting the plastic mechanisms of the alloy, the F1-score, Recall, Precision, Accuracy, and Area Under the ROC Curve (AUC) were 0.74, 0.75, 0.80, 0.78, and 0.83, respectively. A near-β titanium alloy, Ti-8V-4Mo-3.5Al-3Cr-2Zr (wt.%), along with an associated heat treatment process, has been developed using this model. The experimental results have demonstrated that the model accurately predicts the strength, elongation, and plasticity mechanisms of the alloys. The TRIP/TWIP effects occur due to {332}<113> twinning and secondary α" martensite formation during tensile deformation. This study offers an alternative approach for designing TRIP/TWIP near-β titanium alloys.