Linyuxuan Li

The large-format, long-distance welding of heat exchanger plates is widely used in shipbuilding, oil and gas, power and metallurgical equipment, rail transportation, and other fields. To address issues such as low automation and information silos in actual welding production, this paper proposes an intelligent weld seam identification and trajectory generation method, accurately achieving coordinate generation for large-format, long-distance heat exchanger plate welding. The method investigates a camera calibration model based on coordinate transformation, preprocesses collected weld seam images, develops an edge approximation algorithm using median filters for denoising, and proposes a two-stage fusion strategy of “deep learning localization + optimized operator refinement” for edge intelligent identification. This strategy utilizes deep learning object detection for the fast and robust coarse localization of weld regions, combined with optimized operators for high-precision, efficient pixel-level edge extraction. Finally, a weld trajectory coordinate generation program based on the Hough transform algorithm is developed, enabling the rapid automatic welding of plates by welding robots. Experiments demonstrate the accurate identification of heat exchanger plate welds with an error of only 0.33%, meeting welding requirements. The method shows the fast integration of identification and welding information: overall welding efficiency improved by over 100%, and we achieved strong real-time performance, compatibility, and low hardware requirements.