Version 1
: Received: 13 September 2024 / Approved: 13 September 2024 / Online: 13 September 2024 (11:50:11 CEST)
How to cite:
Kirda, A. W.; Majewski, P.; Bursy, G.; Bartoszuk, M.; Yassin, H.; Krolczyk, G.; Akbar, N. A.; Caesarendra, W. Integrating YOLOv5, Jetson Nano, and Mitsubishi Manipulator for Real-time Machine Vision Application in Manufacturing. Preprints2024, 2024091083. https://doi.org/10.20944/preprints202409.1083.v1
Kirda, A. W.; Majewski, P.; Bursy, G.; Bartoszuk, M.; Yassin, H.; Krolczyk, G.; Akbar, N. A.; Caesarendra, W. Integrating YOLOv5, Jetson Nano, and Mitsubishi Manipulator for Real-time Machine Vision Application in Manufacturing. Preprints 2024, 2024091083. https://doi.org/10.20944/preprints202409.1083.v1
Kirda, A. W.; Majewski, P.; Bursy, G.; Bartoszuk, M.; Yassin, H.; Krolczyk, G.; Akbar, N. A.; Caesarendra, W. Integrating YOLOv5, Jetson Nano, and Mitsubishi Manipulator for Real-time Machine Vision Application in Manufacturing. Preprints2024, 2024091083. https://doi.org/10.20944/preprints202409.1083.v1
APA Style
Kirda, A. W., Majewski, P., Bursy, G., Bartoszuk, M., Yassin, H., Krolczyk, G., Akbar, N. A., & Caesarendra, W. (2024). Integrating YOLOv5, Jetson Nano, and Mitsubishi Manipulator for Real-time Machine Vision Application in Manufacturing. Preprints. https://doi.org/10.20944/preprints202409.1083.v1
Chicago/Turabian Style
Kirda, A. W., Nur Arifin Akbar and Wahyu Caesarendra. 2024 "Integrating YOLOv5, Jetson Nano, and Mitsubishi Manipulator for Real-time Machine Vision Application in Manufacturing" Preprints. https://doi.org/10.20944/preprints202409.1083.v1
Abstract
(1) Background: Efficient detection and rectification of defects or post-processing manufacturing conditions such as sharp edges and burrs on metal components are crucial for quality control in precision manufacturing industries. (2) Methods: This paper describes a lab-scale integrated system for real-time and automated metal edge image detection using a customized YOLOv5 ma-chine vision algorithm. The YOLOv5 algorithm and model were developed and embedded in the NVIDIA Jetson Nano microprocessor. The YOLOv5 model can detect three different image types for classification i.e., normal, sharp, and burrs edge on aluminum workpiece blocks. An integrated system connects the NVIDIA Jetson Nano with an embedded YOLOv5 model to a Mitsubishi Electric Melfa RV-2F-1D1-S15 manipulator to perform selective automated chamfer-ing and grinding when certain condition defects are detected. (3) Results: The model demonstrates durable performance, achieving a mean average precision of 0.886 across defect classes with minimal misclassifications. The Mitsubishi Electric Melfa RV-2F-1D1-S15 manipulator received input from the machine vision system and performed an automated chamfering and grinding process accordingly; (4) Conclusions: By integrating camera, embedded deep learning in the microprocessor and manipulator, automated chamfering and grinding process in metal component shapes can be efficiently rectified. This tailored solution promises to improve productivity and consistency in metal manufacturing and remanufacturing.
Keywords
automated chamfering and grinding; deep learning, intelligent manufacturing and remanufacturing; machine vision; robotic manufacturing
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
