Deep Learning-Based YOLO Applied to Rear Weld Pool Thermal Monitoring of Metallic Materials in the GTAW Process

This study investigates the use of YOLOv8 deep learning models to segment and classify thermal images acquired from the rear of the weld pool during the Gas Tungsten Arc Welding (GTAW) process. Thermal data were acquired using a two-color pyrometer under three welding current levels (160 A, 180 A, and 200 A). Models of sizes from nano to extra-large were trained on 66 annotated frames and evaluated with and without data augmentation. The results demonstrate that the YOLOv8m model achieved the best classification performance, with a precision of 83.25% and an inference time of 21.4 ms per frame by using GPU, offering the optimal balance between accuracy and speed. Segmentation accuracy also remained high across all current levels. The YOLOv8n model was the fastest (15.9 ms/frame) but less accurate (75.33%). Classification was most reliable at 160 A, where the thermal field was more stable. The arc reflection class was consistently identified with near-perfect precision, demonstrating the model’s robustness against non-relevant thermal artifacts. These findings confirm the feasibility of using lightweight, dual-task neural networks for reliable weld pool analysis, even with limited training data.