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.

线材电弧增材制造（WAAM）由于其成本低、沉积速率高、材料利用率高等特点，近年来逐渐在工业应用中得到应用。WAAM 工艺中的异常情况，例如夹杂物、孔隙率和未熔合，可能会对最终产品的质量产生不可预测的影响。虽然一些研究研究了 WAAM 过程中的异常检测方法，但它们主要依赖于需要大量手动标记的监督学习方法，而对无监督模型的关注较少。此外，大多数研究集中于实际生产中罕见的重大异常，限制了其实际应用。本文提出了一种两阶段无监督缺陷检测基于在线熔池视频数据的框架。通过考虑制造过程的运动特性，采用改进的阈值方法来检测WAAM 过程中的异常情况。结合机器上下文信息，通过人机交互界面进一步识别和显示缺陷的物理空间位置。本研究中使用的数据集源自 WAAM 零件的真实打印过程。与基线方法相比，所提出的方法显着提高了召回率，并在测试集上实现了 86.3% 的 F1 分数。