Adaptive Domain-Enhanced Transfer Learning for Welding Defect Classification

The integration of Intelligent Welding Systems (IWS) in smart manufacturing leverages advancements in sensors, robotics, and artificial intelligence to optimize welding processes. However, in industry practice, we still face challenges such as sufficient data is not available for every manufacturing task, the costs associated with welding data annotation quality, and the risk of knowledge forgetting during the continual welding process. To tackle these issues, we developed an Adaptive Domain-Enhanced Transfer Learning (ADETL) framework that integrates self-supervised and continual learning strategies. This framework is adept at using incremental and unlabeled data for pre-training, in which we analyze the parameter space, loss landscape, and make the model understand the behaviour of knowledge transfer from diverse source domains. The ADETL framework improves the performance of defect classification, offering a promising solution to the challenges inherent in automatic, continuous welding operations.