Hybrid Statistical Process Monitoring of Wire Arc Additive Manufacturing With Frequency-Informed Deep Learning

Arc welding is classified as a special process under ISO standards, making process monitoring a critical component of the welding and additive manufacturing (AM) certification procedure. Nowadays, the advancements in data analysis have led to the growing use of Machine Learning (ML) techniques for real-time weld quality assessment. However, due to their simple design and minimal data requirements, traditional statistical process monitoring (SPM) methods, such as control charts, remain widely used for evaluating process quality and detecting anomalies. Despite their significance, traditional SPM techniques struggle when dealing with multivariate and high-frequency data typical of Industry 4.0 contexts, making their application challenging and highlighting the need for new approaches to data analysis. Therefore, in this study, we propose an innovative hybrid deep learning–based SPM technique for in situ monitoring of the wire arc additive manufacturing (WAAM) process, with the aim of making SPM more effective in this setting. In particular, an experimental campaign was conducted using the Invar36 alloy, and an online anomaly detection application was developed using ML methods to improve the performance of SPM. Specifically, a frequency-informed convolutional auto-encoder (FICA) is used as a sensor fusion technique for welding current and welding voltage data. The obtained latent space across additional temporal dimensions—which fuse the high-frequency information in a low dimensional space—is then analysed using an exponentially weighted moving average (EWMA) chart to detect anomalies during production. The results demonstrate that the proposed methodology improves anomaly detection performance compared to conventional SPM techniques, with the F2-score improving from 71.1% to 81.3%.