A Two-Stage Welding Control System Combining Rule-Based and Data-Driven Vision

With the development of welding automation and intelligence, computer vision-based welding process monitoring technologies have become increasingly applied. However, interference factors such as welding fumes, spatter, and intense arc light in the welding environment significantly affect the performance of existing welding monitoring and control technologies. To address this issue, this paper proposes a novel method for extracting molten pool dimensions that integrates rule-based and data-driven vision approaches and introduces a welding speed control system that considers the stability of the welding process. The goal of this study is to develop a method that not only monitors the molten pool’s size but also evaluates its stability. In this system, the first stage improves the accuracy of molten pool recognition and dimension extraction through an enhanced method, while the second stage introduces a speed adjustment factor based on welding stability to achieve stable control of welding speed. Experimental results demonstrate that the proposed method shows high adaptability and extraction accuracy under various interference conditions. The welding speed control strategy significantly enhances the stability and symmetry of the welding process, with performance improvements of approximately 45% in stability compared to the conventional method. This leads to an overall improvement in weld quality.