Abstract
This study proposes a novel mechatronics system capable of automating a peg-in-hole assembly process and inspecting the quality of the assembly with vision. The proposed mechatronics system integrates a customized peg insertion tool, a new assembly mechanism, and a control algorithm to efficiently insert pegs into holes with a tolerance of 200 µm. The system improves assembly performance by utilizing dual cameras and several computer vision techniques, including Contrasted Limited Adaptive Histogram Equalization, Canny Edge Detector, Hough Circle Transform, and YOLOv5. In addition, a real-time statistical quality inspection method is proposed and compared with machine learning-based inspection approaches. Various surface textures and materials of the cylindrical workpiece are used to assess the robustness of the proposed inspection methods. Experimental results demonstrate that the proposed system and quality inspection methods exhibit high performance and adaptability.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.