Weld Image Processing Algorithm Based on Machine Vision Detection and Industrial Internet of Things

Abstract

In order to solve the problem of inaccurate centerline extraction caused by external environmental interference in traditional algorithms for processing weld seam images, which in turn affects the accuracy of robotic arm welding, the author proposes a weld seam image processing algorithm based on machine vision detection and industrial Internet of Things. This method first preprocesses the welding object image by grayscale, histogram equalization, and threshold segmentation. Then, the Canny operator is used to extract the pixel edge lines of the weld seam, and a dedicated centerline extraction algorithm for the weld seam is designed. Finally, an adaptive polynomial welding seam curve fitting algorithm was proposed, and the pixel coordinates of the fitting curve were converted into robot coordinates through a coordinate transformation model, in order to send welding path data to the arc welding robot. The experimental results show that the improved arc welding robot system can autonomously recognize complex weld seam trajectories and generate accurate welding paths, thereby guiding the robot to complete welding operations. The maximum error value is controlled within 0.29 millimeters, which meets the requirements of welding accuracy and shows good processing effect.