Multi-stage Inspection of Laser Welding Defects using Machine Learning

As welding processes become faster and components consist of many more welds compared to previous applications, there is a need for fast but still precise quality inspection. The aim of this paper is to compare already existing approaches, namely single-sensor systems (SSS) and multi-sensor systems (MSS) with a proposed cascaded system (CS). The introduced CS is characterized by the fact that not all available data are analyzed, but only cleverly selected ones. The different approaches consisting of neural networks are compared in terms of their accuracy and computational effort. The data are recorded from scratch and include two common sensor systems for quality control, namely a photodiode (PD) and a high-speed camera (HSC). As a result, when the CS makes half of the final decisions based on a SSS with PD signals and the other half based on a SSS with HSC images, the estimated computational effort is reduced by almost 50% compared to the SSS with HSC images as input. At the same time, the accuracy decreases only by 0.25% to 95.96%. Additionally, based on the CS, a general cascaded system (GCS) for quality inspection is proposed.