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

Wire arc additive manufacturing (WAAM) is an additive manufacturing process for building large-sized metal components using gas metal arc welding technology. Detecting defects during deposition is critical for halting production of low-quality components, thereby reducing waste and associated costs, and allowing timely process adjustments. This necessitates the integration of online anomaly detection systems in modern WAAM systems. While most existing methods rely on high-frequency data acquisition, this research explores the feasibility of using low-cost and low-frequency acquisition systems for anomaly detection, leveraging the advancements in Industry 4.0 and IoT protocols like MQTT. 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. The methodology involves using a data-driven model to forecast WAAM process variables and detect anomalies through a Gaussian mixture model based on estimation errors between model estimation and real values collected from the process. The results obtained from various models were compared, specifically polynomial autoregression with exogenous variables (polyARX), autoregressive neural network with exogenous variables (ARXNN), and long short-term memory (LSTM). The proposed system demonstrated high performance in anomaly detection, achieving an F2-score of 90.4% with the LSTM model and 86.28% with the ARXNN model when used to forecast the WAAM process. These results are comparable to performance reached with supervised machine learning algorithms or with results reached by previous studies which employ high-frequency data. Furthermore, the results show that low-frequency data, when processed with complex ML techniques, can reduce the chance of missing anomalies by 40% compared to traditional statistical methods, thanks to a higher recall. Finally, the implementation of the system using containerisation technologies like Docker was discussed, and post-deployment results confirmed the findings. Additionally, a graphical user interface was developed for operator interaction, utilising emerging IoT technologies such as Grafana and SQL databases, which represent additional parts of the IoT-driven intelligent WAAM system proposed in this work.