EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 415 (2025) MATEC Web Conf., 415 (2025) 03005 References
Open Access
Issue
MATEC Web Conf.
Volume 415, 2025
International Colloquium on Mechanical and Civil Engineering (ICMCE’2025)
Article Number 03005
Number of page(s) 11
Section Artificial Intelligence and Optimization
DOI https://doi.org/10.1051/matecconf/202541503005
Published online 27 October 2025
  1. T. Ji and X. Xu, ‘Exploring the Integration of cloud manufacturing and cyber-physical systems in the era of industry 4.0 – An OPC UA approach’, Robot. Comput.-Integr. Manuf., vol. 93, p. 102927, Jun. 2025, doi: 10.1016/j.rcim.2024.102927. [Google Scholar]
  2. Q. Qi and F. Tao, ‘Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison’, IEEE Access, vol. 6, pp. 3585–3593, 2018, doi: 10.1109/ACCESS.2018.2793265. [CrossRef] [Google Scholar]
  3. M. Shaw, ‘Highly Integrated Solutions for Tire Pressure Monitoring Systems’, presented at the Asia Pacific Automotive Engineering Conference, Aug. 2007, pp. 2007-01–3638. doi: 10.4271/2007-01-3638. [Google Scholar]
  4. F. Gow, L. Guan, and J. Park, ‘TPMS Tire Localization with RF Transceiver’, presented at the WCXTM 17: SAE World Congress Experience, Mar. 2017, pp. 2017-01–1638. doi: 10.4271/2017-01-1638. [Google Scholar]
  5. M. C. Cyrille et al., ‘ÉTUDE MULTI-ÉCHELLE DE L’INTERACTION PNEUCHAUSSÉE DANS DES CONDITIONS DE ROULEMENT BASÉE SUR LE COUPLAGE DE LA MÉTHODE DES ÉLÉMENTS FINIS ET DE LA MÉTHODE DES ÉLÉMENTS DISCRETS’. [Google Scholar]
  6. Y. Wang, C. Sicot, J. Borée, and M. Grandemange, ‘Experimental study of wheel-vehicle aerodynamic interactions’, J. Wind Eng. Ind. Aerodyn., vol. 198, p. 104062, Mar. 2020, doi: 10.1016/j.jweia.2019.104062. [Google Scholar]
  7. J. Yan, Y. Meng, L. Lu, and L. Li, ‘Industrial Big Data in an Industry 4.0 Envi-ronment: Challenges, Schemes, and Applications for Predictive Maintenance’, IEEE Access, vol. 5, pp. 23484–23491, 2017, doi: 10.1109/ACCESS.2017.2765544. [Google Scholar]
  8. J.-R. Ruiz-Sarmiento, J. Monroy, F.-A. Moreno, C. Galindo, J.-M. Bonelo, and J. Gonzalez-Jimenez, ‘A predictive model for the maintenance of industrial machinery in the context of industry 4.0’, Eng. Appl. Artif. Intell., vol. 87, p. 103289, Jan. 2020, doi: 10.1016/j.engappai.2019.103289. [Google Scholar]
  9. P. D. Paraschos, G. K. Koulinas, and D. E. Koulouriotis, ‘Reinforcement Learning-Based Optimization for Sustainable and Lean Production within the Context of Industry 4.0’, Algorithms, vol. 17, no. 3, p. 98, Feb. 2024, doi: 10.3390/a17030098. [Google Scholar]
  10. V. Karkaria, J. Chen, C. Luey, C. Siuta, D. Lim, and R. Radulescu, ‘A Digital Twin Framework Utilizing Machine Learning for Robust Predictive Maintenance: Enhancing Tire Health Monitoring’, J Comput Inf Sci Eng. [Google Scholar]
  11. V. Karkaria, Y.-K. Tsai, Y.-P. Chen, and W. Chen, ‘An optimization-centric review on integrating artificial intelligence and digital twin technologies in manufac-turing’, Eng. Optim., vol. 57, no. 1, pp. 161–207, Jan. 2025, doi: 10.1080/0305215X.2024.2434201. [Google Scholar]
  12. E. Bakopoulos, V. Siatras, P. Mavrothalassitis, N. Nikolakis, and K. Alexopou-los, ‘Digital-Twin-Enabled Framework for Training and Deploying AI Agents for Production Scheduling’, in Artificial Intelligence in Manufacturing, J. Soldatos, Ed., Cham: Springer Nature Switzerland, 2024, pp. 147–179. doi: 10.1007/978-3-031-46452-2_9. [Google Scholar]
  13. I. A. Daniyan, K. Mpofu, and A. O. Adeodu, ‘Development of a diagnostic and prognostic tool for predictive maintenance in the railcar industry’, Procedia CIRP, vol. 90, pp. 109–114, 2020, doi: 10.1016/j.procir.2020.02.001. [Google Scholar]
  14. O. Serradilla, E. Zugasti, J. Ramirez De Okariz, J. Rodriguez, and U. Zurutuza, ‘Adaptable and Explainable Predictive Maintenance: Semi-Supervised Deep Learn-ing for Anomaly Detection and Diagnosis in Press Machine Data’, Appl. Sci., vol. 11, no. 16, p. 7376, Aug. 2021, doi: 10.3390/app11167376. [Google Scholar]
  15. M. Tamborski, I. Rojek, and D. Mikołajewski, ‘Revolutionizing Tire Quality Control: AI’s Impact on Research, Development, and Real-Life Applications’, Appl. Sci., vol. 13, no. 14, p. 8406, Jul. 2023, doi: 10.3390/app13148406. [Google Scholar]
  16. J. E. J., V. K. D., D. S. A. S., P. M., and S. T. A., ‘Deep Learning-based Predictive Maintenance for Industrial IoT Applications’, in 2024 International Conference on Inventive Computation Technologies (ICICT), Lalitpur, Nepal: IEEE, Apr. 2024, pp. 1197–1202. doi: 10.1109/ICICT60155.2024.10544377. [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.