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

This study proposes an unsupervised anomaly detection technique for Wire Arc Additive Manufacturing (WAAM) based on deep learning, namely 1D-Convolutional AutoEncoder, and demonstrates a significant 54.8% improvement in anomaly detection performance compared to similar methods.