In-situ monitoring and online prediction of keyhole depth in laser welding by coaxial imaging
: Núñez, Henrique H.L.; Hsu, Li-Wei; Barros Ribeiro, Kandice; Salminen, Antti; Moreira Bessa, Wallace
: Schmidt, M.; Arnold, C. B.; Wudy, K.
: CIRP Conference on Photonic Technologies
Publisher: Elsevier BV
: 2024
: Procedia CIRP
: 13th CIRP Conference on Photonic Technologies [LANE 2024]
: Procedia CIRP
: 124
: 793
: 796
: 2212-8271
DOI: https://doi.org/10.1016/j.procir.2024.08.227(external)
: https://doi.org/10.1016/j.procir.2024.08.227(external)
: https://research.utu.fi/converis/portal/detail/Publication/458392701(external)
A comprehensive understanding of welding penetration and the role of process parameters is crucial for ensuring high-quality joints in laser welding. In-situ process monitoring can aid in detection of defects, reducing material usage and time-consuming inspection operations. In this study, we propose a novel approach for online prediction of keyhole depth in laser welding operations. Using in-process images captured with a coaxial camera and active illumination, our software employs pre-Trained CNNs from the EfficientNet and DenseNet families to extract features. These features serve as input for data-efficient regression models, trained to predict the keyhole depth. The results have shown that both methods yield percentage errors of approximately 3%. Ultimately, this methodology facilitates real-Time analysis of welding operations.
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