by G. Mattera, J. Polden, A. Caggiano, P. Commins, L. Nele, Z. Pan
Abstract
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 realtime 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%. 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.
Keywords: wire arc additive manufacturing; anomaly detection; machine learning; defect detection
Video presentation
Presenting author
 | Name: Affiliation: Email: | Giulio Mattera University of Naples Federico II, Italy giulio.mattera@unina.it |
