A study on rocker arm defect inspection based on vision system

Abstract

The rocker arm plays a crucial role in an internal combustion engine’s valve train system. Issues often arise when there are errors in the production of the rocker arm, such as the following: (1) misalignment of the rocker arm spindle hole and thread hole; and (2) bevel errors in the rocker arm spindle hole. This paper focuses on the development of a rocker arm chamfering and coaxial defect inspection system, which utilizes the RANSAC algorithm. The algorithm consists of five steps: (i) representing a 2D point cloud as a matrix with X and Y coordinates; (ii) detecting the number of circles present in the input point cloud; (iii) setting a tolerance threshold for the distance between the selected circle and other points, crucial for achieving high accuracy; (iv) specifying the maximum number of iterations to find the best model from the input points; (v) defining the maximum number of points that can belong to the same circle, considering point density and the anticipated circle’s surface. The real-time inspection system is designed with a camera mounted on top of the box, and two LED arrays positioned around the box at suitable distances. Test results using a collected data set demonstrate the effectiveness and accuracy of the proposed method in detecting each type of error. In the experiments, the proposed method successfully detected all coaxial defect samples and achieved a detection rate of 95.1% for chamfer defect samples.