Automatic welding seam tracking and real-world coordinates identification with machine learning method

In the automatic welding process, the precision of the welding position significantly influences welding quality, relying heavily on the accurate identification of the welding seam. However, in complex and unstructured welding environments, such as those with strong arc lights, welding splashes, and thermal-induced deformations, various disturbances present a significant challenge to identify the welding seam. This paper introduces a cost-effective welding seam tracking model constructed using a laser line and a fixed viewpoint single RGB camera. The model's primary objective is to detect a chain of welding-seam points in both captured images and real-time camera feeds. The paper presents the techniques for camera calibration, laser detection, and the use of artificial intelligence to establish a real-world coordinate frame. The process begins with camera calibration using the OpenCV library to filter noise and undistort images. Subsequently, the thresholding technique is employed for color image segmentation, isolating the laser sensor light on the workpiece. An algorithm is then introduced to detect welding spots. Finally, the image coordinates are converted to world coordinates using two machine learning methods: Random Forest Regression and Gradient Boosting Regression. Experimental demonstrations were conducted at different positions on the object to assess algorithm precision. The results indicate average deviations of 0.83 mm, 0.74 mm, and 0.77 mm for X, Y, and Z coordinates, respectively, which fall below the expected standard deviation of 3 mm. Despite the overall consistency of the program, slight noise from environmental lighting may still exist. In conclusion, the proposed welding seam tracking method and coordinate conversion program have proven effective and cost-efficient, with potential for future improvements.