Development of an Accurate and Automated Quality Inspection System for Solder Joints on Aviation Plugs Using Fine-Tuned YOLOv5 Models

Abstract

The quality inspection of solder joints on aviation plugs is extremely important in modern manufacturing industries. However, this task is still mostly performed by skilled workers after welding operations, posing the problems of subjective judgment and low efficiency. To address these issues, an accurate and automated detection system using fine-tuned YOLOv5 models is developed in this paper. Firstly, we design an intelligent image acquisition system to obtain the high-resolution image of each solder joint automatically. Then, a two-phase approach is proposed for fast and accurate weld quality detection. In the first phase, a fine-tuned YOLOv5 model is applied to extract the region of interest (ROI), i.e., the row of solder joints to be inspected, within the whole image. With the sliding platform, the ROI is automatically moved to the center of the image to enhance its imaging clarity. Subsequently, another fine-tuned YOLOv5 model takes this adjusted ROI as input and realizes quality assessment. Finally, a concise and easy-to-use GUI has been designed and deployed in real production lines. Experimental results in the actual production line show that the proposed method can achieve a detection accuracy of more than 97.5% with a detection speed of about 0.1 s, which meets the needs of actual production