Abstract
Additive Manufacturing (AM) technology has gained widespread application across various industries due to its capability to directly produce products from computer-aided design models. Among AM techniques, the Electron Beam Selective Melting (EBSM) process has attracted significant attention, particularly in aerospace and automotive industries, owing to its high precision, speed, and excellent material properties. However, various defects, especially internal defects that inevitably arise during the manufacturing process, significantly limit the performance of EBSM parts. In this study, X-ray computed tomography (CT) was utilized to scan EBSM parts, and cross-sectional images were employed to train several state-of-the-art modern object detection models for evaluating their effectiveness in detecting internal defects. Sparse R-CNN demonstrated the best overall performance, while the YOLO series excelled in specific metrics. To further capitalize on the strengths of different detection models, a model ensemble approach, Selective Box Fusion (SBF) is proposed. This approach employs voting and weighted fusion of detection boxes to mitigate errors inherent in individual models. Experimental results show that the SBF ensemble method effectively integrates the advantages of multiple detection models, leading to improvements across various evaluation metrics compared to individual models and other ensemble methods.