A two-stage unsupervised approach for surface anomaly detection in wire and arc additive manufacturing

Wire and arc additive manufacturing (WAAM) has gradually been applied in industrial applications in recent years due to its low cost, high deposition rate, and high material utilization rate. Anomalies in the WAAM process, such as inclusion, porosity, and lack of fusion, can have unpredictable effects on the quality of the final product. While some studies have investigated anomaly detection methods in the WAAM process, they mainly rely on supervised learning methods that require extensive manual labeling, with less attention paid to unsupervised models. Furthermore, most studies focus on significant anomalies that are rare in actual production, limiting their practical application. This paper proposes a two-stage unsupervised defect detection framework based on online melt pool video data. By considering the motion characteristics of the manufacturing process, a revised threshold method is used to detect anomalies during the WAAM process. Combining machine contextual information, the physical spatial location of defects is further identified and displayed through a human-machine interactive interface. The dataset used in this study is derived from real printing processes of WAAM parts. Compared with baseline methods, the proposed approach significantly improves recall and achieves an F1-score of 86.3% on the test set.