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

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.