Abstract:  Deep neural network techniques based on supervised learning have been extensively employed in the field of welding. However, in real industrial scenarios, obtaining labeled data samples is challenging, which limits the performance of deep network models. For unlabeled or partially labeled datasets, transfer learning algorithms offer a novel solution. Transfer learning algorithms in terms of domain adaptation and pre-training-fine- tuning are introduced and a summary of current research on their development over recent years and their applications in weld penetration identification and defect detection is provided. And transfer learning issues in welding that require further attention and exploration in the futureis highlighted. Transfer learning methods can enhance the effectiveness of deep learning models by better utilizing existing data and knowledge in the welding field, specifically in weld penetration recognition and welding defect detection accuracy. This promotes the development of intelligent welding manufacturing technology. 

Key words: welding, transfer learning, welding penetration, welding defect, deep learning