Data-driven fatigue life prediction in additive manufactured titanium alloy: A damage mechanics based machine learning framework

Additive manufacturing (AM) technology has been widely employed in the fabrication of titanium alloy parts for aerospace engineering applications. In this paper, a damage mechanics based machine learning framework is presented for the data-driven fatigue life prediction of AM titanium alloy. At first, a theoretical framework including the damage mechanics based fatigue models and random forest model is presented for the fatigue damage analysis and life prediction of the AM titanium alloys under cyclic loadings. Second, a computational methodology is demonstrated in detail from two aspects, that is, the numerical implementation of the damage mechanics based fatigue models and the construction process of the random forest model. After that, fatigue life predictions are carried out for the AM titanium alloy smooth and notched specimens under different stress levels and stress ratios. The predicted results are compared with the experimental data to verify the proposed method. Finally, parametric studies are investigated on the prediction performance and fatigue lives for the AM titanium alloys.

Automated summary

This paper proposes a machine learning framework based on damage mechanics for data-driven fatigue life prediction of AM titanium alloy. Fatigue life predictions are conducted for AM titanium alloy specimens under different stress levels and stress ratios, compared with experimental data, and parametric studies on prediction performance and fatigue lives are carried out.