Abstract
References
- Marafona JDM, Marques PMT, Martins RC and Seabra JHO. Mesh stiffness models for cylindrical gears: A detailed review, Mechanism and Machine Theory, 2021;166.
- Feng K, Ji JC, Ni Q and Beer M. A review of vibration-based gear wear monitoring and prediction techniques, Mech Syst Signal Process, 2023;182.
- Virtanen EPK, Szanti G, Amanov A and Kanerva MS. Investigation of bevel gears failure modes, Engineering Failure Analysis, 2024;165.
- Düzcükoğlu H. Study on development of polyamide gears for improvement of load-carrying capacity, Tribology International, 2009;42(8):1146–53.
- Wu D, Yan P, Zhou H, Liu T, Fang Y and Yi R. A novel online framework for gear machining quality prediction based on ensemble deep regression, Measurement, 2022;201.
- Lv Y, Cui B, Sun Z and Xiao X. Effects of discrete laser surface melting on the fatigue performance of 20CrMnTi steel gear, Optics and Laser Technology, 2024;171.
- Chen T, Zhu C, Liu H, Wei P, Zhu J and Xu Y. Simulation and experiment of carburized gear scuffing under oil jet lubrication, Engineering Failure Analysis, 2022;139.
- Chen T, Zhu C, Chen J and Liu H. A review on gear scuffing studies: Theories, experiments and design, Tribology International, 2024;196.
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- Scharf HP, Cambraia HN and da Costa DD. A new embedded vision system for monitoring tool conditions in production lines using a combination of direct and indirect methods, Journal of Manufacturing Processes, 2023;102:143-153.
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- Olorunfemi BO, Nwulu NI, Adebo OA and Kavadias KA. Advancements in machine visions for fruit sorting and grading: A bibliometric analysis, systematic review, and future research directions, Journal of Agriculture and Food Research, 2024;16.
- Gan N, Wang Y, Ren G, Li M, Ning J and Zhang Z. Design and testing of a machine-vision-based air-blow sorting platform for famous tea fresh leaves production, Computer and Electronics in Agriculture, 2023;214.
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Abstract
Gears, one of the indispensable components used in the industry, are mechanical elements that ensure efficient energy transmission, altering the speed and torque of rotational movements. The reliability and durability of gears directly affect the overall performance of related systems. Recently, gear manufacturing has been nearly fully automated with the help of advanced technology. However, it is common to assess the quality of a gear via traditional methods. The conventional quality control techniques for gear quality determination cause many difficulties, such as time-consuming and user-dependent measurement errors. In short, these conventional measurement methods decrease manufacturing speed. Today, Machine Vision Systems (MVS) offer the possibility to advance automated quality control systems. In this paper, to save time and reduce user-dependent errors, an automated gear evaluation system was developed for integration into a mass production line. The developed system has a rotating table, with gears progressing on the table at a controllable rotating speed. The gears are inspected for common defects such as missing teeth, rough surfaces, incorrect diameters, and other flaws. The detection process uses an MVS, programmed to differentiate perfect gears from defective ones through a vision system. The detected defective gears are automatically separated by pushing from the production line using compressed air via a pneumatic valve. This system enhances the efficiency of the production line and prevents defective gears from advancing to subsequent stages of production or assembly. As a result of the experiment, the standard deviation of both defective and perfect gears was measured below 1%, which is an indication of high measurement precision. The developed system provides high-speed quality control in mass production processes, thus aiming to increase efficiency by minimizing user-dependent measurement errors on mass production lines.
Keywords
References
- Marafona JDM, Marques PMT, Martins RC and Seabra JHO. Mesh stiffness models for cylindrical gears: A detailed review, Mechanism and Machine Theory, 2021;166.
- Feng K, Ji JC, Ni Q and Beer M. A review of vibration-based gear wear monitoring and prediction techniques, Mech Syst Signal Process, 2023;182.
- Virtanen EPK, Szanti G, Amanov A and Kanerva MS. Investigation of bevel gears failure modes, Engineering Failure Analysis, 2024;165.
- Düzcükoğlu H. Study on development of polyamide gears for improvement of load-carrying capacity, Tribology International, 2009;42(8):1146–53.
- Wu D, Yan P, Zhou H, Liu T, Fang Y and Yi R. A novel online framework for gear machining quality prediction based on ensemble deep regression, Measurement, 2022;201.
- Lv Y, Cui B, Sun Z and Xiao X. Effects of discrete laser surface melting on the fatigue performance of 20CrMnTi steel gear, Optics and Laser Technology, 2024;171.
- Chen T, Zhu C, Liu H, Wei P, Zhu J and Xu Y. Simulation and experiment of carburized gear scuffing under oil jet lubrication, Engineering Failure Analysis, 2022;139.
- Chen T, Zhu C, Chen J and Liu H. A review on gear scuffing studies: Theories, experiments and design, Tribology International, 2024;196.
- Montironi MA, Castellini P, Stroppa L and Paone N. Adaptive autonomous positioning of a robot vision system: Application to quality control on production lines, Robotics and Computer-Integrated Manufacturing, 2014;30(5):489–98.
- Bono FM, Radicioni L and Cinquemani S. A novel approach for quality control of automated production lines working under highly inconsistent conditions, Engineering Applications of Artificial Intelligence, 2023;122.
- Scharf HP, Cambraia HN and da Costa DD. A new embedded vision system for monitoring tool conditions in production lines using a combination of direct and indirect methods, Journal of Manufacturing Processes, 2023;102:143-153.
- Reichenstein T, Raffin T, Sand C and Franke J. Implementation of Machine Vision based Quality Inspection in Production: An Approach for the Accelerated Execution of Case Studies, Procedia CIRP, 2022;112:596-601.
- Olorunfemi BO, Nwulu NI, Adebo OA and Kavadias KA. Advancements in machine visions for fruit sorting and grading: A bibliometric analysis, systematic review, and future research directions, Journal of Agriculture and Food Research, 2024;16.
- Gan N, Wang Y, Ren G, Li M, Ning J and Zhang Z. Design and testing of a machine-vision-based air-blow sorting platform for famous tea fresh leaves production, Computer and Electronics in Agriculture, 2023;214.
- Zhou W, Li Y, Liu L, Wang H and You M. Cork classification based on multi-scale faster-RCNN with machine vision, Measurement. 2023;217.
- Lu Z, Zhao M, Luo J, Wang G and Wang D. Design of a winter-jujube grading robot based on machine vision, Computer and Electronics in Agriculture, 2021;186.
- Rezaei P, Hemmat A, Shahpari N and Mireei SA. Machine vision-based algorithms to detect sunburn pomegranate for use in a sorting machine, Measurement. 2024;232.
Details
| Primary Language | English |
|---|---|
| Subjects | Mechanical Engineering (Other) |
| Journal Section | Articles |
| Authors | |
| Publication Date | June 30, 2025 |
| Submission Date | September 11, 2024 |
| Acceptance Date | January 13, 2025 |
| Published in Issue | Year 2025 Volume: 8 Issue: 1 |
Cite
An international scientific e-journal published by the University of Usak