A unified probabilistic defect-based fatigue modelling approach to account for surface and bulk defects in additively manufactured Ti-6Al-4V alloys and defect characterisation

Additively manufactured metals are often affected by material inhomogeneities across multiple length scales. Geometrical inhomogeneities, as well as non-metallic inclusions — commonly referred to as defects — can significantly impair the fatigue performance of the fabricated material, especially in the absence of further post-processing. Therefore, accounting for these defects is essential for a proper assessment of the material’s structural integrity. Fracture Mechanics-based approaches have proven effective in addressing bulk and sub-surface defects; however, a consensus has yet to be reached on the most effective method for addressing surface geometrical inhomogeneities (e.g., surface roughness). This work presents the effectiveness of a generalised defect-based model capable of accounting for bulk, sub-surface, and surface defect (roughness) effects on the fatigue endurance limit. The proposed model is calibrated on a detailed experimental fatigue characterisation study of a Ti-6Al-4V alloy manufactured through two different techniques, i.e., Electron Beam Melting (EBM) and Selective Laser Melting (SLM). To achieve varied defect characteristics and quantitatively assess their influence on fatigue, the additively manufactured materials were subjected to different combinations of post-processing (e.g., Hot Isostatic Pressing, Vacuum Heat Treatment). Defect distributions were investigated using the non-destructive lab-based Computed Tomography (CT), and further data analysis was conducted by exploiting the Extreme Value Statistics (EVS) theory. The approach was effective across all studied material conditions, providing a probabilistic assessment of fatigue failure performance.