S&M3084 Research Paper of Special Issue
Published: October 31, 2022
Development of Automated Optical Inspection and Classification Systems [PDF]
Pu-Sheng Tsai, Ter-Feng Wu, Jen-Yang Chen, and Chia-Luen Tsai
(Received July 4, 2022; Accepted October 4, 2022)
Keywords: manipulator, machine vision, attitude control, optical detection, identification and classification
In this study, we propose a novel system for automatic optical inspection (AOI) by integrating the Dobot Magician four-axis manipulator and machine vision. In the proposed system, the attitude control for the end joint rotates the claw kit to clamp the workpiece moving on the conveyor belt and performs color classification. The system conducts feature detection by examining the workpiece from different angles through cameras by using the working platform Python. Furthermore, the system identifies the positions of detected features and the center point and inclination angle of the workpiece. Finally, the Dobot Magician manipulator moves to the appropriate clamping position to pick up the object and then moves the object to the specified position. In this study, objects are preliminarily classified on the conveyor belt by using a manipulator through a photoelectric switch and a color recognition sensor, and the Hough transform is used to calculate the center point and rotation angle of the object for image recognition. To integrate image processing and Dobot manipulator motion control to complete the automatic simulation platform, an AOI setup is built for a production line. The system cooperates with the Python extension kit Tkinter to establish a user-friendly human–machine interface environment, which promotes the development and applicability of the automatic production platform.Corresponding author: Ter-Feng Wu
This work is licensed under a Creative Commons Attribution 4.0 International License.
Cite this article
Pu-Sheng Tsai, Ter-Feng Wu, Jen-Yang Chen, and Chia-Luen Tsai, Development of Automated Optical Inspection and Classification Systems, Sens. Mater., Vol. 34, No. 10, 2022, p. 3895-3910.