An effective approach for identifying fatigue-critical defects from X-ray 3D reconstruction: Example in L-PBF AlSil0Mg alloys

Internal defects have been widely believed as the physical origins of fatigue crack initiation and growth in additively manufactured (AM) metals, and the specific defect that triggers final failure typically dictates the overall fatigue life of critical safety equipment. To this regard, reliable identification of such critical defects among numerous of these imperfections is essential for accurate fatigue life prediction and defect-tolerant design. In this study, we investigate the geometric and spatial nature of porosity defects in laser powder bed fusion AlSi10Mg alloys, including their size, position, morphology, and orientation, and attempt elucidating their effect on fatigue resistance using high-resolution X-ray computed tomography (X-CT) and post-mortem fractographic analysis. Recognizing that image-based finite element analysis can be computationally intensive and that conventional defect descriptor may not fully capture the complexity of defect geometry and spatial context, we propose an effective Critical Defect Ranking Function (CDRF) metric that quantitatively integrates defect size, location, morphology, and orientation directly from large 3D X-CT imaging data. An effective defect size is adopted to ensure physical consistency, with the enhanced detrimental effect of near-surface defects considered. The CDRF enables direct, automated identification and ranking of fatigue-critical defects, and demonstrates predictive correlation with post-mortem experimental results. This robust, non-destructive framework facilitates defect-based reliability assessment and quality assurance in AM components.