On-Line Monitoring System of Welding Quality Based on Machine Vision and Machine Learning

In this paper, an online monitoring system of welding quality based on machine vision and machine learning was proposed. A high-speed CCD camera was used to monitor the tail end of the molten pool, and the remove small objects algorithm and contour compensation based on convex hull algorithm were utilized to achieve high-precision collection of features such as the width and length of the tail of the molten pool. This effectively solved the technical challenges caused by welding splashes and plasma arc, which could interfere with visual acquisition. Combined with neural network algorithms, a welding quality model was established and validated to accurately identify defects such as welding undercut, welding deviations, and unstable welding processes, with a defect recognition rate of
$\geq 94\%$
.