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Wire arc additive manufacturing (WAAM) has received attention because of its high deposition rate, low cost, and high material utilization. However, quality issues are critical in WAAM because it builds upon arc welding technology, which can result in low precision and poor quality of the melted parts. Hence, anomaly detection is essential for identifying abnormal behaviors and process instability during WAAM to reduce the time and cost of post-process treatment. The relevant studies have been conducted on anomaly detection algorithms using machine learning in fused deposition modeling and laser powder bed fusion; however, they have less investigated the implementation for in situ quality monitoring in WAAM. This work presents a real-time anomaly detection method that uses a convolutional neural network (CNN) in WAAM. The proposed method enables creation of CNN-based models that detect …