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All issues Volume 306 (2020) MATEC Web Conf., 306 (2020) 02005 References
Open Access
Issue
MATEC Web Conf.
Volume 306, 2020
The 6th International Conference on Mechatronics and Mechanical Engineering (ICMME 2019)
Article Number 02005
Number of page(s) 6
Section Mechanical Design and Manufacturing System
DOI https://doi.org/10.1051/matecconf/202030602005
Published online 14 January 2020
  1. Conry R D. Compressor: U.S. Patent 5,857, 348(1999) [Google Scholar]
  2. Boschert S, Rosen R. Digital twin—the simulation aspect (Mechatronic Futures, Cham, 2016) [Google Scholar]
  3. Giannoulis D, Massberg M, Reiss J D. Digital dynamic range compressor design—A tutorial and analysis, Journal of the Audio Engineering Society 60(6), 399–408 (2012) [Google Scholar]
  4. Castellanos I D, Stine J E. Compressor trees for decimal partial product reduction[C]//Proceedings of the 18th ACM Great Lakes symposium on VLSI. ACM, 107-110 (2008) [Google Scholar]
  5. Prytz R, Nowaczyk S, Rögnvaldsson T, et al. Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data, Engineering applications of artificial intelligence, 41,139-150 (2015) [Google Scholar]
  6. El Saddik A. Digital twins: The convergence of multimedia technologies, IEEE MultiMedia, 25(2), 87–92 (2018) [Google Scholar]
  7. West T D, Blackburn M. Is Digital Thread/Digital Twin Affordable? A Systemic Assessment of the Cost of DoD’s Latest Manhattan Project, Procedia computer science, 114, 47-56 (2017) [Google Scholar]
  8. Fourgeau E, Gomez E, Adli H, et al. System engineering workbench for multi-views systems methodology with 3DEXPERIENCE Platform. the aircraft radar use case (Complex Systems Design & Management Asia, Cham, 2016) [Google Scholar]
  9. Vogel-Heuser B, Konersmann M, Aicher T, et al. Supporting evolution of automated material flow systems as part of CPPS by using coupled meta models, 2018 IEEE Industrial Cyber-Physical Systems (ICPS), 316–323 (2018) [Google Scholar]
  10. Gray D, Bowes D, Davey N, et al. The misuse of the NASA metrics data program data sets for automated software defect prediction[C]//15th Annual Conference on Evaluation & Assessment in Software Engineering. IET, 96-103 (2011) [Google Scholar]
  11. Glaessgen E, Stargel D. The digital twin paradigm for future NASA and US Air Force vehicles[C]//53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA, 1818(2012) [Google Scholar]
  12. Schleich B, Anwer N, Mathieu L, et al. Shaping the digital twin for design and production engineering, CIRP Annals, 66(1), 141–144 (2017) [CrossRef] [Google Scholar]
  13. Tao F, Zhang M. Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing, Ieee Access, 5, 20418-20427 (2017) [Google Scholar]
  14. Tao F, Cheng J, Qi Q, et al. Digital twin-driven product design, manufacturing and service with big data, The International Journal of Advanced Manufacturing Technology, 94(9–12), 3563–3576 (2018) [Google Scholar]
  15. Negri E, Fumagalli L, Macchi M. A review of the roles of digital twin in cps-based production systems, Procedia Manufacturing, 11, 939-948 (2017) [Google Scholar]
  16. Schleich B, Anwer N, Mathieu L, et al. Shaping the digital twin for design and production engineering, CIRP Annals, 66(1), 141–144 (2017) [Google Scholar]
  17. Zhang H, Liu Q, Chen X, et al. A digital twin-based approach for designing and multi-objective optimization of hollow glass production line, IEEE Access, 5, 26901-26911 (2017) [Google Scholar]
  18. Wang X, Shi Z, Zhang F, et al. Mutual trust based scheduling for (semi) autonomous multi-agent systems[C]//2015 American Control Conference (ACC). IEEE, 459-464 (2015) [Google Scholar]
  19. Leng J, Zhang H, Yan D, et al. Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop, Journal of Ambient Intelligence and Humanized Computing, 10(3), 1155–1166 (2019) [Google Scholar]
  20. Zhang K, Qu T, Zhou D, et al. IoT-enabled dynamic lean control mechanism for typical production systems, Journal of Ambient Intelligence and Humanized Computing, 10(3), 1009–1023 (2019) [Google Scholar]
  21. Simpson T W, Williams C B, Hripko M. Preparing industry for additive manufacturing and its applications: Summary & recommendations from a National Science Foundation workshop, Additive Manufacturing, 13, 166-178 (2017) [Google Scholar]
  22. Salter J D, Campbell C, Journeay M, et al. The digital workshop: Exploring the use of interactive and immersive visualisation tools in participatory planning, Journal of environmental management, 90(6), 2090-2101 (2009) [CrossRef] [Google Scholar]
  23. Orchard S, Hermjakob H, Julian Jr R K, et al. Common interchange standards for proteomics data: Public availability of tools and schema. Report on the Proteomic Standards Initiative Workshop, 2nd Annual HUPO Congress, Montreal, Canada, 8–11th October 2003, Proteomics, 4(2), 490–491 (2004) [Google Scholar]
  24. Li B, Hou B, Yu W, et al. Applications of artificial intelligence in intelligent manufacturing: a review, Frontiers of Information Technology & Electronic Engineering, 18(1), 86–96 (2017) [Google Scholar]
  25. Gao Q, Shi R, Wang G. Construction of intelligent manufacturing workshop based on lean management, Procedia CIRP, 56, 599-603 (2016) [Google Scholar]

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