Abstract
Directed energy deposition (DED) has emerged as a significant metal AM process with a variety of established applications, such as component repair, coatings, and multi-material structures. Nonetheless, the formation of volumetric defects during the printing process is one of the most challenging obstacles to the widespread adoption of DED. These volumetric defects accelerate the onset of cracks, subsequent propagation, and ultimate failure of components, making them one of the primary contributors to poor mechanical properties, especially fatigue properties, with a high priority in critical load-bearing applications. Unraveling fatigue life behavior with different volumetric defect structures provides helpful insights for attempts to diminish their adverse effects. In this study, novel defect features were proposed, capturing different aspects of volumetric defect structure. These features include compactness, extent, elongation, and gap. The first three features, compactness, extent, and elongation, represent different characteristics attributed to the shape of volumetric defects. Therefore, they were named shape descriptors. The last feature, which is the gap, characterizes the location of each specific volumetric defect with respect to its surrounding defects. In fact, it shows how close volumetric defects are to each other. It is worth mentioning that all defect features were investigated for the first time in DED. To explore the linkages between defect characteristics and fatigue life, several stainless steel 316L samples were fabricated by varying process parameters to induce different distributions of volumetric defects. X-ray computed tomography (XCT) was conducted to capture the distribution of volumetric defects for each sample. After heat treatment to homogenize the microstructure of the samples, fatigue tests were performed to determine the fatigue life of each sample. The proposed defect characteristics were computed with the aid of XCT data and then analyzed using a novel statistical approach. In this statistical methodology, the statistical distribution of each defect feature for each sample was first characterized. A unique shape of distribution was observed for each defect feature. For instance, the gap showed a leptokurtic distribution. Next, statistical descriptors, including average, median, standard deviation, skewness, and kurtosis, were calculated for each defect feature. Interestingly, strong correlations were observed among these statistical descriptors and fatigue lives. By increasing average and median compactness, fatigue life improved. Additionally, average extent, median extent, average elongation, and median elongation showed a similar effect on fatigue life. For elongation, fatigue life decreased with increasing standard deviation and skewness. Gap was another feature besides elongation with a significant effect on fatigue life. Fatigue life increased by increasing the average gap and kurtosis and decreased by increasing the standard deviation and skewness.
