Recurrent Autoencoder for Weld Discontinuity Prediction

Abstract

Laser beam butt welding is often the technique of choice for a wide range of industrial tasks. To achieve high quality welds, manufacturers often rely on heavy and expensive clamping systems to limit the sheet movement during the welding process, which can affect quality. Jiggless welding offers a cost-effective and highly flexible alternative to common clamping systems. In laser butt welding, the process-induced joint gap has to be monitored in order to counteract the effect by means of an active position control of the sheet metal. Various studies have shown that sheet metal displacement can be detected using inductive probes, allowing the prediction of weld quality by ML-based data analysis. The probes are dependent on the sheet metal geometry and are limited in their applicability to complex geometric structures. Camera systems such as long-wave infrared (LWIR) cameras can instead be mounted directly behind the laser to overcome a geometry dependent limitation of the jiggles system. In this study we will propose a deep learning approach that utilizes LWIR camera recordings to predict the remaining welding process to enable an early detection of weld interruptions. Our approach reaches 93.33% accuracy for time-wise prediction of the point of failure during the weld.