Two-stage quality monitoring of a laser welding process using machine learning

Abstract In production, quality monitoring is essential to detect defective elements. State-of-the-art approaches are single-sensor systems (SSS) and multi-sensor systems (MSS). Yet, these approaches might not be suitable: Nowadays, one component may comprise several hundred meters of the weld seam, necessitating high-speed welding to produce enough components. To detect as many defects as possible in time, fast yet precise monitoring is required. However, information captured by SSS might not be sufficient and MSS suffer from long inference times. Therefore, we present a confidence-based cascaded system (CS). The key idea of the CS is that not all data are analyzed to obtain the quality weld, but only selected ones. As evidenced by our results, all CS outperform SSS in terms of accuracy and inference time. Further, compared to MSS, the CS has hardware advantages.