Detecting process instabilities in industrial gas metal arc welding time series

Abstract

Gas metal arc welding is widely used in industrial series production for joining aluminum. A lot of factors, such as instabilities and complex dependencies, influence the quality of the resulting welding seams. It is challenging to identify the causes of welding defects, and the real reason is not always well understood. Ensuring the process stability helps production workers to increase the overall production efficiency. The process stability increases the process repeatability, so the welding performance is optimized and rejects are avoided. This paper presents a technique to detect process instabilities within the multivariate process variables automatically. An autoencoder architecture is implemented. The latent space of the autoencoder and reconstruction of the time series are used to detect process instabilities. Detected issues are visualized in a heatmap, including supportive metrics to describe deviations from the expected behavior. As a result, the proposed architecture supports process optimization and leads to an increase in production transparency.