By incorporating the defect feature information, an ML-based linkage between defects and fatigue life unaffected by the time scale is developed, the primary focus is to quantitatively assess and elucidate the impact of different defect features on fatigue life.
A machine learning (ML) framework is proposed to predict the fatigue life of LPBF-built Hastelloy X utilizing microstructural defects identified through nondestructive detection prior to fatigue testing. The proposed method combines nondestructive micro-computerized tomography (micro-CT) technique to comprehensively analyze the size, location, morphology and distribution of the defects.
In the test set, SVM-based fatigue life prediction exhibits the highest accuracy. Regarding the defect information, the defect size significantly affects fatigue life, and the diameter of the circumscribed sphere of the largest defect has a critical effect on fatigue life.
This comprehensive approach provides valuable insights into the fatigue mechanism of structural materials in defective states, offering a novel perspective for better understanding the influence of defects on fatigue performance.
