Frequency informed convolutional autoencoder for in situ anomaly detection in wire arc additive manufacturing

Giulio Mattera (), Mario Vozza, Joseph Polden, Luigi Nele and Zengxi Pan
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Giulio Mattera: University of Naples Federico II
Mario Vozza: Polytechnic University of Turin
Joseph Polden: University of Wollongong
Luigi Nele: University of Naples Federico II
Zengxi Pan: University of Wollongong

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 8, No 27, 5819-5834

Abstract: Abstract In the context of Industry 4.0, the importance of anomaly detection is growing, particularly in Additive Manufacturing, as it allows for the detection and localization of defects, thereby reducing waste and costs. However when normal and anomaly signals have similar shapes in time this task is particularly challenging. Despite that, the frequency content of time series signals often holds valuable information that, when integrated into the learning process, can greatly improve the recognition of hidden patterns in the data and enhance feature separability. In this study, we propose an unsupervised anomaly detection technique for Wire Arc Additive Manufacturing (WAAM) based on deep learning, namely 1D-Convolutional AutoEncoder. By integrating frequency-regularization terms based on wavelet analysis of defect-free welding signals during the training phase, the results demonstrated a significant 54.8% improvement in anomaly detection performance compared to similar methods. This improvement enables the effective use of unsupervised learning for anomaly detection in WAAM, minimizing the need for labeled data and making it suitable for industrial applications, even when dealing with unbalanced datasets.

Keywords: Machine learning; Autoencoder; Anomaly detection; Wire arc additive manufacturing; Frequency domain (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-024-02507-y

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