Continuous Adaptation of Deep Learning Models for Optical Quality Monitoring Tasks

Deep learning based classification models, such as convolutional neural networks (CNN), show high potential for the automation of visual quality monitoring tasks. However, the performance of these models depends on the availability of a suitable database for their training. In the manufacturing domain the generation of such data comes with significant expenses. Especially in high-mix low-volume production, where processes and occurring defect patterns frequently change, the time required for data generation currently hinders the application of deep learning methods. Therefore, we propose a concept for continuous adaptation of deep learning models during their application. By combining an automatic detection of predictions with a high failure probability and continual learning approaches, overall efforts for data generation can be reduced. Initial investigation of the concept for image-based quality monitoring in friction stir welding (FSW) shows promising results.