Optimizing Cellular Manufacturing Systems: Minimizing Production Time with Collaborative Robots (Cobots) using PSO

Year : 2023 | Volume : 01 | Issue : 01 | Page : 22-27
By

    S. M. Saleemuddin

  1. Sanjeeva Reddy K. Hudgikar

  1. Student, Visvesvaraya Technological University, Belagavi, Karnataka, India
  2. Professor, Sharnabasva University, Karnataka, India

Abstract

This study focuses on the implementation of cobots in a cellular manufacturing system to minimize production time. The use of cobots, collaborative robots capable of working alongside human operators, aims to increase efficiency and reduce manual labor in the manufacturing process. The mathematical model presented in this research evaluates the impact of cobots on production time by considering various factors, including task times, machine setup, inspection, waiting, and idle times. Through the model, we analyze the production time for two scenarios: one with cobots and another without cobots, using real-world data and assumptions. The results demonstrate the potential benefits of integrating cobots into the manufacturing system, leading to shorter production times and increased productivity. Furthermore, this study emphasizes the importance of process analysis, cobot selection, workforce training, and continuous improvement to optimize the cobot integration process. By understanding the implications of using cobots in cellular manufacturing, manufacturers can make informed decisions to streamline their production systems and remain competitive in a dynamic market.

Keywords: Optimizing Cellular Manufacturing Systems: Minimizing Production Time with Collaborative Robots (Cobots) using PSO

[This article belongs to International Journal of Industrial and Product Design Engineering(ijipde)]

How to cite this article: S. M. Saleemuddin, Sanjeeva Reddy K. Hudgikar Optimizing Cellular Manufacturing Systems: Minimizing Production Time with Collaborative Robots (Cobots) using PSO ijipde 2023; 01:22-27
How to cite this URL: S. M. Saleemuddin, Sanjeeva Reddy K. Hudgikar Optimizing Cellular Manufacturing Systems: Minimizing Production Time with Collaborative Robots (Cobots) using PSO ijipde 2023 {cited 2023 Dec 15};01:22-27. Available from: https://journals.stmjournals.com/ijipde/article=2023/view=129648

References

  1. Ali Keshvarparast,et.al.,(2023) Collaborative robots in manufacturing and assembly systems: literature review and future research agenda, Journal of Intelligent Manufacturing(2)
  2. Eloise Matheson,Eloise Matheson .et.al.,(2019).Human–Robot Collaboration in Manufacturing Applications: A Review, 8(4),100,Robotics,MDPI: https://doi.org/10.3390/robotics8040100
  3. Zhang, S., & Wuzhong, L. (2019). Research on the Application of Collaborative Robots in Cellular Manufacturing. In 2019 International Conference on Robots & Intelligent System (ICRIS) (pp. 18-21). IEEE. DOI: 10.1109/ICRIS48471.2019.00008
  4. Li, Y., Zhang, H., Cheng, T., & Zhang, R. (2018). Application of Cobots in Manufacturing: A Review. In 2018 6th International Conference on Machinery, Materials and Computing Technology (ICMMCT) (pp. 82-85). IEEE. DOI: 10.1109/ICMMCT.2018.0020
  5. Kumari, S., Singh, S. K., & Kumar, A. (2019). Integration of Collaborative Robots in Manufacturing Industries. In 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 719-724). IEEE. DOI: 10.1109/SPIN.2019.8711740
  6. Liu, Y., Wang, G., & Wu, Y. (2019). Simulation-based optimization of the layout design for cellular manufacturing systems. International Journal of Production Research, 57(8), 2467-2481. DOI: 10.1080/00207543.2018.1554399
  7. Tjahjono, B., & Barata, E. A. (2020). Performance measurement system for cellular manufacturing system. Procedia Manufacturing, 45, 342-347. DOI: 10.1016/j.promfg.2020.05.042
  8. Wenzel, S., Nyhuis, P., & Stricker, N. (2018). Process optimization and human-robot collaboration in a manufacturing cell. Procedia CIRP, 72, 328-333. DOI: 10.1016/j.procir.2018.03.272
  9. Sakthivel, P., Ramesh, M. V., & Suresh, N. (2017). Modelling and optimization of cellular manufacturing system using metaheuristics. International Journal of Industrial and Systems Engineering, 25(1), 1-22. DOI: 10.1504/IJISE.2017.10010102
  10. (2022a). Sales value of the industrial robotics market worldwide from 2018 to 2022a, by application area (in million U.S. dollars) [Graph]. In Statista. Retrieved March 08, 2022a, from https://www.statista.com/statistics/1018262/industrial-robotics-sales-value-worldwide-by-application-area/
  11. Navas-Reascos GE, Romero D, Rodriguez CA, Guedea F, Stahre J. (2022) Wire harness assembly process supported by a collaborative robot: A case study focus on ergonomics. Robotics. Nov 16, 11(6), 131.
  12. Patton, J., Brown, D. A., Peshkin, M., Santos-Munné, J. J., Makhlin, A., Lewis, E. & Schwandt, D. (2008). KineAssist: design and development of a robotic overground gait and balance therapy device. Topics in stroke rehabilitation, 15(2), 131-139.

Regular Issue Subscription Review Article
Volume 01
Issue 01
Received July 5, 2023
Accepted August 1, 2023
Published December 15, 2023