Hostname: page-component-848d4c4894-x5gtn Total loading time: 0 Render date: 2024-05-22T17:51:54.717Z Has data issue: false hasContentIssue false
  • English
  • Français

TOWARDS REMOTE CONTROL OF MANUFACTURING MACHINES THROUGH ROBOT VISION SENSORS

Published online by Cambridge University Press:  19 June 2023

Nourhan Halawi Ghoson ,
Nisar Hakam ,
Zohreh Shakeri ,
Vincent Meyrueis ,
Stéphane Loubère  and
Khaled Benfriha
Nourhan Halawi Ghoson*
Affiliation:
Arts et métiers - science et technologie;
Nisar Hakam
Affiliation:
Arts et métiers - science et technologie;
Zohreh Shakeri
Affiliation:
Arts et métiers - science et technologie;
Vincent Meyrueis
Affiliation:
Arts et métiers - science et technologie;
Stéphane Loubère
Affiliation:
PwC | Strategy&
Khaled Benfriha
Affiliation:
Arts et métiers - science et technologie;
*
Halawi Ghoson, Nourhan, Arts et métiers - science et technologie, Lebanon (Lebanese Republic), nourhan.halawi_ghoson@ensam.eu

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The remote management of equipment is part of the functionalities granted by the design principles of Industry 4.0. However, some critical operations are managed by operators, machine setup and initialization serve as a significant illustration. Since the initialization is a repetitive task, industrial robots with a smart vision system can undertake these duties, enhancing the autonomy and flexibility of the manufacturing process. The smart vision system is considered essential for the implementation of several characteristics of Industry 4.0. This paper introduces a novel solution for controlling manufacturing machines using an embedded camera on the robot. This implementation requires the development of an interactive interface, designed in accordance with the supervision system known as Manufacturing Execution System. The framework is implemented inside a manufacturing cell, demonstrating a quick response time and an improvement between the cameras.

Keywords

Design engineeringDesign for Additive Manufacturing (DfAM)Design processIndustrial robotsSmart vision sensors
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 3601 - 3610
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

References

Al-Kindi, G. and Zughaer, H. (2012a) ‘An Approach to Improved CNC Machining Using Vision-Based System’, Materials and Manufacturing Processes, 27(7), pp. 765–774. Available at: https://doi.org/10.1080/10426914.2011.648249.CrossRefGoogle Scholar
Al-Kindi, G. and Zughaer, H. (2012b) ‘Intelligent Vision-Based Computerized Numerically Controlled (CNC) Machine’, in Lee, G. (ed.) Advances in Automation and Robotics, Vol. 2. Berlin, Heidelberg: Springer (Lecture Notes in Electrical Engineering), pp. 619–628. Available at: https://doi.org/10.1007/978-3-642-25646-2_80.Google Scholar
Benfriha, K. et al. (2021) ‘Development of an advanced MES for the simulation and optimization of industry 4.0 process’, International Journal for Simulation and Multidisciplinary Design Optimization, 12. Available at: https://doi.org/10.1051/smdo/2021022.CrossRefGoogle Scholar
Computer Vision and Industry 4.0: Value, tools, examples of use (no date). Available at: https://www.n-ix.com/computer-vision-manufacturing/ (Accessed: 22 June 2022).Google Scholar
D'Avella, S., Avizzano, C.A. and Tripicchio, P. (2023) ‘ROS-Industrial based robotic cell for Industry 4.0: Eye-in-hand stereo camera and visual servoing for flexible, fast, and accurate picking and hooking in the production line’, Robotics and Computer-Integrated Manufacturing, 80, p. 102453. Available at: https://doi.org/10.1016/j.rcim.2022.102453.CrossRefGoogle Scholar
El Hamdi, S., Abouabdellah, A. and Oudani, M. (2019) ‘Industry 4.0: Fundamentals and Main Challenges’, in 2019 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA). 2019 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA), pp. 1–5. Available at: https://doi.org/10.1109/LOGISTIQUA.2019.8907280.CrossRefGoogle Scholar
Ge, J., Chi, Z. and Li, Q. (2014) ‘Small Part Assembly with Dual Arm robot and Smart Camera’, in ISR/Robotik 2014; 41st International Symposium on Robotics. ISR/Robotik 2014; 41st International Symposium on Robotics, pp. 1–6.Google Scholar
Golnabi, H. and Asadpour, A. (2007) ‘Design and application of industrial machine vision systems’, Robotics and Computer-Integrated Manufacturing, 23(6), pp. 630–637. Available at: https://doi.org/10.1016/j.rcim.2007.02.005.CrossRefGoogle Scholar
el Helou, M., et al. (2022) ‘A Modular Smart Vision System for Industrial Inspection and Control of Conformity’, 6, p. 13. Available at: https://doi.org/10.1520/SSMS20220003.CrossRefGoogle Scholar
Kaczmarczyk, V. et al. (2022) ‘Revisiting the Role of Manufacturing Execution Systems in Industry 4.0’, IFAC-PapersOnLine, 55(4), pp. 151–157. Available at: https://doi.org/10.1016/j.ifacol.2022.06.025.CrossRefGoogle Scholar
Kurada, S. and Bradley, C. (1997) ‘A review of machine vision sensors for tool condition monitoring’, Computers in Industry, 34(1), pp. 55–72. Available at: https://doi.org/10.1016/S0166-3615(96)00075-9.CrossRefGoogle Scholar
Lasi, H. et al. (2014) ‘Industry 4.0’, Business & Information Systems Engineering, 6(4), pp. 239–242. Available at: https://doi.org/10.1007/s12599-014-0334-4.CrossRefGoogle Scholar
Liu, Z. et al. (2022) ‘Robot learning towards smart robotic manufacturing: A review’, Robotics and Computer-Integrated Manufacturing, 77, p. 102360. Available at: https://doi.org/10.1016/j.rcim.2022.102360.CrossRefGoogle Scholar
Lou, P. et al. (2022) ‘Real-time monitoring for manual operations with machine vision in smart manufacturing’, Journal of Manufacturing Systems, 65, pp. 709–719. Available at: https://doi.org/10.1016/j.jmsy.2022.10.015.CrossRefGoogle Scholar
Mantravadi, S. and Møller, C. (2019) ‘An Overview of Next-generation Manufacturing Execution Systems: How important is MES for Industry 4.0?’, Procedia Manufacturing, 30, pp. 588–595. Available at: https://doi.org/10.1016/j.promfg.2019.02.083.CrossRefGoogle Scholar
Mühlbauer, M. et al. (2022) ‘An Approach to Progress Monitoring of Industrial Manual Processes Based on Camera Recordings and Object Interactions’, Procedia CIRP, 107, pp. 582–587. Available at: https://doi.org/10.1016/j.procir.2022.05.029.CrossRefGoogle Scholar
Oh, J.-K. and Lee, C.-H. (2007) Development of a stereo vision system for industrial robots, p. 663. Available at: https://doi.org/10.1109/ICCAS.2007.4406981.CrossRefGoogle Scholar
Penumuru, D.P., Muthuswamy, S. and Karumbu, P. (2020) ‘Identification and classification of materials using machine vision and machine learning in the context of industry 4.0’, Journal of Intelligent Manufacturing, 31(5), pp. 1229–1241. Available at: https://doi.org/10.1007/s10845-019-01508-6.CrossRefGoogle Scholar
Sergiyenko, O. et al. (2018) ‘Machine Vision Sensors’, Journal of Sensors, 2018, pp. 1–2. Available at: https://doi.org/10.1155/2018/3202761.CrossRefGoogle Scholar
Shakeri, Z. and Benfriha, K. (2022) An Overview on Smart Scheduling in MES: In the Context of Industry 4.0. Available at: https://doi.org/10.1109/ICAC55051.2022.9911166.CrossRefGoogle Scholar
Wang, L. (2019) ‘From Intelligence Science to Intelligent Manufacturing’, Engineering, 5(4), pp. 615–618. Available at: https://doi.org/10.1016/j.eng.2019.04.011.CrossRefGoogle Scholar
Zouhal, Z. et al. (2021) Approach for industrial inspection in the context of Industry 4.0. Available at: https://doi.org/10.1109/ICECCME52200.2021.9591119.Google Scholar