A X-Ray Based Dual-Expert Detection Method for Automatic Welding Defect Inspection

Abstract

Automatic welding technology has been widely applied in the field of industrial production. In promoting high-quality intelligent manufacturing processes for the consumer market, precise detection of welding defects is a key link in enhancing product reliability. Considering the high demand for domain knowledge in welding defect inspection tasks, existing methods have shortcomings in fine-grained recognition. To overcome these weaknesses, this article proposes a dual-expert detection method for automatic welding defect inspection, inspired by the human multi-expert complementary evaluation process. The method process is mainly reflected in the following aspects. Firstly, a high-precision robot welding defect inspection system that integrates application platform based data collection, model training, and deployment has been effectively implemented. Then, diverse detection experts are constructed using complementary regression paradigms to obtain predictions from multiple perspectives. Finally, a dual-expert fusion scheme based on probabilistic ensembling is designed to fully integrate prediction information and obtain refined results. In the experiment, a database rich in diverse types of welding defects is applied for training and systematic testing, and multiple sets of refined indicators are evaluated. In addition, we also discussed edge deployment, failure case analysis, and the application of in consumer PCB defect detection. These practical application results indicate that the proposed method can achieve product level implementation, effectively reducing the threshold for industrial testing and enabling end-users to gain benefits.