Welding robot automation technology based on digital twin

Abstract

In the era of intelligence and automation, robots play a significant role in the field of automated welding, enhancing efficiency and precision. However, challenges persist in scenarios demanding complexity and higher precision, such as low welding planning efficiency and inaccurate weld seam defect detection. Therefore, based on digital twin technology and kernel correlation filtering algorithm, a welding tracking model is proposed. Firstly, the kernel correlation filtering algorithm is used to train the filter on the first frame of the collected image, determine the position of image features in the region, extract histogram features of image blocks, and then train the filter using ridge regression to achieve welding trajectory tracking. Additionally, an intelligent weld seam detection model is introduced, employing a backbone feature network for feature extraction, feature fusion through a feature pyramid, and quality detection of weld seams through head classification. During testing of the tracking model, the maximum tracking error is −0.232 mm, with an average absolute tracking error of 0.08 mm, outperforming other models. Comparatively, in tracking accuracy, the proposed model exhibits the fastest convergence with a precision rate of 0.845, surpassing other models. In weld seam detection, the proposed model excels with a detection accuracy of 97.35% and minimal performance loss at 0.023. In weld seam quality and melt depth error detection, the proposed model achieves errors within the range of −0.06 mm, outperforming the other two models. These results highlight the outstanding detection capabilities of the proposed model. The research findings will serve as technical references for the development of automated welding robots and welding quality inspection.