Pore-induced fatigue failure: A prior progressive fatigue life prediction framework of laser-directed energy deposition Ti-6Al-4V based on machine learning.

• A prior progressive fatigue life prediction framework of L-DED Ti-6Al-4V was established based on machine learning. • Stress, pore types, size, position, shape, and the existence of FGA are the main factors influencing fatigue life of L-DED Ti-6Al-4V. • The proposed fatigue life framework exhibited strong generalization capability, robustness and efficiency. Pores are major cause of fatigue failure in laser-directed energy deposition (L-DED) titanium alloy. For the safe application of L-DED titanium alloys, it is essential to establish a fatigue life prediction method based on pore-induced fatigue. This paper proposes a prior progressive fatigue life prediction framework based on ridge classification and kernel ridge regression algorithms. The fatigue life prediction was carried out on L-DED Ti-6Al-4V alloy in three steps: critical pore identification, fine granular area existence prediction and final fatigue life prediction. The fatigue life prediction method adopted in the current study outperform the others with a correlation coefficient as high as 0.951, followed by a comparison with the results derived from different machine learning algorithms. The results show that the proposed fatigue life prediction framework can predict the fatigue life of L-DED Ti-6Al-4V alloy based on computed tomography tests and microstructure features. Due to its strong generalization ability and effectiveness, the proposed prediction method is expected to be valuable for fatigue-resistant design of L-DED Ti-6Al-4V alloy. [ABSTRACT FROM AUTHOR]