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All issues Volume 249 (2018) MATEC Web Conf., 249 (2018) 03010 References
Open Access
Issue
MATEC Web Conf.
Volume 249, 2018
2018 5th International Conference on Mechanical, Materials and Manufacturing (ICMMM 2018)
Article Number 03010
Number of page(s) 6
Section Mechanical Engineering and Digital Manufacturing
DOI https://doi.org/10.1051/matecconf/201824903010
Published online 10 December 2018
  1. A. Vijayaraghavan, W. Sobel, A. Fox, D. Dornfeld, and P. Warndorf, “Improving machine tool interoperability using standardized interface protocols: MTconnect,” Laboratory for Manufacturing and Sustainability (2008) [Google Scholar]
  2. “Influxdb, version 1.3.” [Online], https://www.influxdata.com/time-seriesplatform/influxdb (2017). [Google Scholar]
  3. J. Gray, K. T. Moore, and B. A. Naylor, “Openmdao: An open source framework for multidisciplinary analysis and optimization,” in AIAA/ISSMO Multidisciplinary Analysis Optimization Conference Proceedings, vol. 5 (2010). [Google Scholar]
  4. N. Matloff, “Introduction to discrete-event simulation and the simpy language,” Davis, CA. Dept of Computer Science. University of California at Davis. Retrieved on August, vol. 2, p. 2009 (2008). [Google Scholar]
  5. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830 (2011). [Google Scholar]
  6. Bokeh Development Team, Bokeh: Python library for interactive visualization (2014). [Google Scholar]
  7. Grafana: the open platform for analytics and monitoring (2017). [Google Scholar]
  8. S. Jeschke, C. Brecher, T. Meisen, D. O¨ zdemir, and T. Eschert, “Industrial internet of things and cyber manufacturing systems,” in Industrial Internet of Things, pp. 3–19, Springer (2017). [CrossRef] [Google Scholar]
  9. J. Lee, E. Lapira, B. Bagheri, and H.-a. Kao, “Recent advances and trends in predictive manufacturing systems in big data environment,” Manufacturing Letters, vol. 1, no. 1, pp. 38–41 (2013). [CrossRef] [Google Scholar]
  10. J. Lee, B. Bagheri, and H.-A. Kao, “A cyber-physical systems architecture for industry 4.0-based manufacturing systems,” Manufacturing Letters, vol. 3, pp. 18–23 (2015). [CrossRef] [Google Scholar]
  11. A. B. Feeney, S. Frechette, and V. Srinivasan, “Cyber-physical systems engineering for manufacturing,” in Industrial Internet of Things, pp. 81–110, Springer (2017). [CrossRef] [Google Scholar]
  12. S. Friedenthal, R. Griego, and M. Sampson, “Incose model based systems engineering (mbse) initiative,” in INCOSE 2007 Symposium (2007). [Google Scholar]
  13. I. P. Arbizu and C. L. Perez, “Surface roughness prediction by factorial design of experiments in turning processes,” Journal of Materials Processing Technology, vol. 143, pp. 390–396 (2003). [CrossRef] [Google Scholar]
  14. T. O¨ zel and Y. Karpat, “Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks,” International Journal of Machine Tools and Manufacture, vol. 45, no. 4, pp. 467–479 (2005). [CrossRef] [Google Scholar]
  15. P. Suresh, P. V. Rao, and S. Deshmukh, “A genetic algorithmic approach for optimization of surface roughness prediction model,” International Journal of Machine Tools and Manufacture, vol. 42, no. 6, pp. 675–680 (2002). [CrossRef] [Google Scholar]
  16. W. Li and S. Kara, “An empirical model for predicting energy consumption of manufacturing processes: a case of turning process,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 225, no. 9, pp. 1636–1646 (2011). [CrossRef] [Google Scholar]
  17. P. Backus, M. Janakiram, S. Mowzoon, C. Runger, and A. Bhargava, “Factory cycle-time prediction with a data-mining approach,” IEEE Transactions on Semiconductor Manufacturing, vol. 19, no. 2, pp. 252–258 (2006). [CrossRef] [Google Scholar]
  18. R. Bhinge, N. Biswas, D. Dornfeld, J. Park, K. H. Law, M. Helu, and S. Rachuri, “An intelligent machine monitoring system for energy prediction using a gaussian process regression,” in 2014 IEEE International Conference on Big Data (Big Data), pp. 978–986, IEEE (2014). [CrossRef] [Google Scholar]
  19. T. Tolio, M. Sacco, W. Terkaj, and M. Urgo, “Virtual factory: An integrated framework for manufacturing systems design and analysis,” Procedia CIRP, vol. 7, pp. 25–30 (2013). [CrossRef] [Google Scholar]
  20. N. Bengtsson, J. Michaloski, F. Proctor, G. Shao, and S. Venkatesh, “Towards data driven sustainable machining combining mtconnect production data and discrete event simulation,” in Proceedings of the Proceedings of ASME 2010 International Manufacturing Science and Engineering Conference (2010). [Google Scholar]
  21. G. Shao, S.-J. Shin, and S. Jain, “Data analytics using simulation for smart manufacturing,” in Proceedings of the 2014 Winter Simulation Conference, pp. 2192–2203, IEEE Press (2014). [CrossRef] [Google Scholar]
  22. J. Park, K. H. Law, R. Bhinge, N. Biswas, A. Srinivasan, D. A. Dornfeld, M. Helu, and S. Rachuri, “A generalized data-driven energy prediction model with uncertainty for a milling machine tool using gaussian process,” in ASME 2015 International Manufacturing Science and Engineering Conference, pp. V002T05A010–V002T05A010, American Society of Mechanical Engineers (2015). [CrossRef] [Google Scholar]
  23. M. Mar´oti, T. Kecsk´es, R. Keresk´enyi, B. Broll, P. V¨olgyesi, L. Jur´acz, T. Levendovszky, and A´. Le´deczi, “Next generation (meta) modeling: Weband cloud-based collaborative tool infrastructure.,” MPM@MoDELS,vol. 1237, pp. 41–60 (2014). [Google Scholar]
  24. J. Rumbaugh, I. Jacobson, and G. Booch, Unified modeling language reference manual, the. Pearson Higher Education (2004). [Google Scholar]
  25. A´. Le´deczi, A. Bakay, M. Maroti, P. Volgyesi, G. Nordstrom, J. Sprinkle, and G. Karsai, “Composing domain-specific design environments,” Computer, vol. 34, no. 11, pp. 44–51 (2001). [CrossRef] [Google Scholar]
  26. T. Kluyver, B. Ragan-Kelley, F. P´erez, B. E. Granger, M. Bussonnier, J. Frederic, K. Kelley, J. B. Hamrick, J. Grout, S. Corlay, et al., “Jupyter notebooks-a publishing format for reproducible computational workflows.,” in ELPUB, pp. 87–90 (2016). [Google Scholar]
  27. S.-H. Leitner and W. Mahnke, “Opc ua–serviceoriented architecture for industrial applications,” ABB Corporate Research Center (2006). [Google Scholar]

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