Open Access
Issue
EPJ Web Conf.
Volume 354, 2026
19th Global Congress on Manufacturing and Management (GCMM 2025)
Article Number 03005
Number of page(s) 19
Section Robotics, Autonomous Systems, and Smart Inspection
DOI https://doi.org/10.1051/epjconf/202635403005
Published online 02 March 2026
  1. J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, P. Abbeel, Domain randomization for transferring deep neural networks from simulation to the real world. In Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), 23–30 (2017). [Google Scholar]
  2. N. Subramanyam, C.V. Kumar, A.R. Reddy, P. Chandrakanth, Image diagnosis using CNN deep learning model. In: AIP Conference Proceedings, vol. 2802, p. 120005. AIP Publishing LLC (2024). [Google Scholar]
  3. A. Ullah, M. Younas, M. S. Saharudin, Digital twin framework using real-time asset tracking for smart flexible manufacturing system. Machines 13, 37 (2025). [Google Scholar]
  4. A. J. Kovari, A framework for integrating vision transformers with digital twins in Industry 5.0 context. Machines 13, 36 (2025). [Google Scholar]
  5. Ö. B. Çapunaman, P. Farrokhsiar, S. G. Bilén, J. P. Duarte, B. Gürsoy, Vision-based sensing and digital twin technologies in conformal 3D concrete printing. Constr. Robot. 9, 4 (2025). [Google Scholar]
  6. T. R. Saravanan, S. Suvitha, D. Banavath, S. Gogula, J. Upadhyay, M. Sudhakar, AI and machine learning integration in medical assistive robotics. In: Fostering Cross-Industry Sustainability with Intelligent Technologies; IGI Global, pp. 130–151 (2023). [Google Scholar]
  7. G. Wang, C. Zhang, S. Liu, Y. Zhao, Y. Zhang, L. Wang, Multi-robot collaborative manufacturing driven by digital twins: Advancements, challenges, and future directions. J. Manuf. Syst. 82, 333–361 (2025). [Google Scholar]
  8. J. Li, G. Zhou, C. Zhang, J. Hu, F. Chang, A. Matta, Defining a feature-level digital twin process model by extracting machining features from MBD models for intelligent process planning. J. Intell. Manuf. 36, 3227–3248 (2025). [Google Scholar]
  9. G. Vinci-Carlavan, D. Rossit, A. Toncovich, A digital twin for operations management in manufacturing engineering-to-order environments. J. Manuf. Syst. 42, 100679 (2024). [Google Scholar]
  10. H. Z. Yu, W. Li, D. Li, L. J. Wang, Y. Wang, Enhancing additive manufacturing with computer vision: A comprehensive review. Int. J. Adv. Manuf. Technol. 132, 5211–5229 (2024). [Google Scholar]
  11. C. O'Donovan, C. Giannetti, C. J. Pleydell Pearce, Revolutionising the sustainability of steel manufacturing using computer vision. Procedia Comput. Sci. 232, 1729–1738 (2024). [Google Scholar]
  12. I. Yousif, J. Samaha, J. Ryu, R. Harik, Safety 4.0: Harnessing computer vision for advanced industrial protection. Manuf. Lett. 41, 1342–1356 (2024). [Google Scholar]
  13. M. T. B. Touhid, E. Zhu, M. V. Ehteshamfara, S. Yang, Evaluation of digital twin synchronization in robotic assembly using YOLOv8. Int. J. Adv. Manuf. Technol. 134, 871–885 (2024). [Google Scholar]
  14. B. Doroszuk, P. Bortnowski, M. Ozdoba, R. Krol, Calibrating the digital twin of a laboratory ball mill for copper ore milling: Integrating computer vision and discrete element method and smoothed particle hydrodynamics simulations. Minerals 14, 407 (2024). [Google Scholar]
  15. E. Kozin, Operational management of production for car maintenance and repair using digital twin technology. In The Future of Industry: Human-Centric Approaches in Digital Transformation (Springer, Cham, 2024), pp. 205–218. [Google Scholar]
  16. K. B. Prakash, A. Amarkarthik, M. Ravikumar, P. Manoj Kumar, S. Jegadheeswaran, Optimizing performance characteristics of blower for combustion process using Taguchi based grey relational analysis. In: International Conference on Advances in Materials Research, pp. 155–163 (2019). [Google Scholar]
  17. M. Balaji, S. N. Dinesh, S. V. Vetrivel, P. M. Kumar, R. Subbiah, Augmenting agility in production flow through ANP. Mater. Today Proc. 47, 5308–5312 (2021). [Google Scholar]
  18. T. Ravichandra, P. Murugeswari, M. Revathi, S. Senthilkumar, D. S. Rathore, M. Sudhakar, Future of machine learning and robotics in digital technology for hospitality. In: Cutting-Edge Technologies for Business Sectors; IGI Global, pp. 401–428 (2023). [Google Scholar]
  19. N. Senthil Kannan, R. Parameshwaran, P. T. Saravanakumar, P. M. Kumar, M. L. Rinawa, Performance and quality improvement in a foundry industry using fuzzy MCDM and lean methods. Arab. J. Sci. Eng. 47, 15379–15390 (2022). [Google Scholar]
  20. P. Chandra Kanth, M.S. Anbarasi, A generic framework for data analysis in privacy-preserving data mining. In: Computational Intelligence in Data Mining, pp. 653–661. Springer Singapore, Singapore (2019). [Google Scholar]
  21. R. Chitharaj, H. Perumal, M. Almeshaal, P. Manoj Kumar, Optimizing performance of a solar flat plate collector using Box-Behnken design. Sustainability 17, 461 (2025). [Google Scholar]
  22. B. A. Kumar, R. Saminathan, M. Tharwan, M. Vigneshwaran, P. S. Babu, S. Ram, P. M. Kumar, Study on the mechanical properties of a hybrid polymer composite using egg shell powder based bio-filler. Mater. Today Proc. 69, 679–683 (2022). [Google Scholar]
  23. K. R. Kunduru, Y. D. Dwivedi, R. Aruna, G. R. Thippeswamy, S. Selvakumar, M. Sudhakar, Elevating performance for enhancing AI-powered humanoid robots through innovation. In: Applied AI and Humanoid Robotics for the Ultra-Smart Cyberspace; IGI Global, pp. 85–119 (2023). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.