Utilising unsupervised machine learning and IoT for cost-effective anomaly detection in multi-layer wire arc additive manufacturing

This study presents an IoT-driven intelligent system for WAAM, where data is collected at a low frequency of 10 Hz and processed using unsupervised machine learning techniques to develop an anomaly detection service, and shows that low-frequency data, when processed with complex ML techniques, can reduce the chance of missing anomalies by 40% compared to traditional statistical methods.