We characterized manufacturing-induced defects in 316L stainless steels - fabricated by direct metal laser sintering (DMLS) - and investigated their roles in the fatigue behavior of steel parts. The primary defects targeted are porosities, inner cracks, and edge cracks. We used Convolutional Neural Networks (CNNs) to detect and classify these defects and moved toward a machine vision-based metrology technique for metal additive manufacturing (AM). The Moore cyclic loading method was applied to characterize the fatigue behavior of 316L samples. The results indicate a strong correlation between the quality of additive manufacturing, defect levels, and the fatigue properties of the steel samples. Specifically, samples with lower defect levels exhibited significantly higher load endurance and longer life cycles. To further explore the influence of defects on mechanical behavior, we applied image processing techniques to measure the density, size, morphology, and location of defects in the steels. The quantification of AM defects features paves the way for a deeper understanding of microstructure – macro-behavior relations and enhanced fatigue prediction models in additively manufactured steels.
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