Defect Detection and Process Monitoring for Wire Arc Additive Manufacturing Using Machine Learning

Wire Arc Additive Manufacturing (WAAM) is a promising manufacturing technology that has been used to build medium to larger-sized components. The recent progress of Artificial Intelligence (AI) technology has led to Machine Learning (ML) algorithms being widely implemented for modeling, control, monitoring, and simulation processes in WAAM. However, current defect detection systems are limited due to the types of detectable defects, and a real-time micro-defect detection system is yet to be developed. This paper aims to provide an in-depth review of process monitoring approaches suitable for a WAAM system related to defect detections. Particular focus is given to the ML-based monitoring systems, and how they could be implemented into the WAAM process to improve the detecting accuracy, reliability, and efficiency. The paper concludes by discussing the current challenges and future work for developing a real-time monitoring system.