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Defect localization during laser microwelding of battery connectors using long exposure imaging and few-shot learning

Procedia CIRP, vol. 111, pp. 790–795

Abstract

In the production of battery systems for electromobility, even a single weak electrical connection of the battery cells can lead to a failure of the entire stack. During laser welding of dissimilar materials such as aluminum and copper, real-time information about the actual part quality is essential for cost-efficient production and high product quality. To address these needs, long exposure optical imaging is used to generate detailed images during the process based on local near-infrared process emission. The high-resolution images are subsequently processed by using a novel data analysis approach. Existing image classification methods based on supervised learning usually require thousands of labeled images for training. To overcome these shortcomings, a few-shot learning approach based on Siamese Neural Networks is investigated, which require only a few samples to detect critical welding defects. The approach is evaluated on industry-relevant experiments and demonstrates promising results in terms of high defect recognition accuracies.

Authors 5

  1. Fraunhofer Institute for Laser Technology

    Affiliation as printed

    Fraunhofer-Institute for Laser Technology, Steinbachstrasse 15, 52074 Aachen, Germany

  2. Fraunhofer Institute for Laser Technology

    Affiliation as printed

    Fraunhofer-Institute for Laser Technology, Steinbachstrasse 15, 52074 Aachen, Germany

  3. Fraunhofer Institute for Laser Technology

    Affiliation as printed

    Fraunhofer-Institute for Laser Technology, Steinbachstrasse 15, 52074 Aachen, Germany

  4. Fraunhofer Institute for Laser Technology

    Affiliation as printed

    Fraunhofer-Institute for Laser Technology, Steinbachstrasse 15, 52074 Aachen, Germany

  5. Fraunhofer Institute for Laser Technology

    Affiliation as printed

    Fraunhofer-Institute for Laser Technology, Steinbachstrasse 15, 52074 Aachen, Germany

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References 28