Abstract
The quality of welded joints is a critical factor affecting the reliability and service life of agricultural machinery, while conventional inspection methods remain dependent on operator experience and are difficult to automate in serial production. This study aimed to develop and experimentally evaluate an integrated intelligent system for automated weld defect detection using machine vision and a convolutional neural network. The proposed system combines an industrial camera, controlled lighti