Anomaly Detection in Spot Welding in Automotive Industry with Autoencoder Neural Networks

Spot welding is one of the most frequently used material joining techniques in the Automotive Industry. Splashes are an anomalous condition of material expulsion that occurs randomly during the process and since it might result in welds with inadequate quality, it should be avoided. This study uses data of the Spot Welding process of a manufacturing unit that uses BOSCH technology and aims to apply Autoencoders considering a supervised learning approach to identify the occurrence of splashes. Additionally, its goal is to verify if the Autoencoder would outperform traditional techniques in this context when employed to identify rarer anomalies, as anticipated by the studies in the literature review. For this reason, the results for datasets with different anomaly rates are evaluated.