Anomaly Detection of Wire Arc Additively Manufactured Parts via Surface Tension Transfer through Unsupervised Machine Learning Techniques

Wire Arc Additive Manufacturing (WAAM) has recently gained significant attention from the research community as it offers potential for notable time and cost reduction compared to other technologies. To enhance the overall quality of products, the ability to detect defects in real-time is a subject of great interest. Accordingly, this work investigates the effectiveness of diverse semi-supervised anomaly detection algorithms based on machine learning for online defect detection in WAAM. Deposition data in terms of welding voltage and current during a Surface Tension Transfer welding process on mild steel samples are used. Twelve statistical features are extracted in the time and frequency domains to identify defects as anomalies with a sample rate of 1 s with a maximum achieved accuracy of 91.9%. The obtained results provide valuable insights into the efficacy of machine learning for online defect detection in WAAM, which can be leveraged to enhance product quality and reduce costs.