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Journal of Engineering
Volume 2016, Article ID 8569694, 11 pages
http://dx.doi.org/10.1155/2016/8569694
Research Article

Changing States of Multistage Process Chains

1West Virginia University, Morgantown, WV, USA
2University of Strathclyde, Glasgow G1 1XQ, UK
3University of Bremen, 28359 Bremen, Germany

Received 5 July 2016; Revised 25 October 2016; Accepted 1 November 2016

Academic Editor: Luis Carlos Rabelo

Copyright © 2016 Thorsten Wuest et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. E. Brinksmeier, Prozeß- und Werkstückqualität in der Feinbearbeitung, Fortschritt-Berichte VDI, Reihe 2: Fertigungstechnik, VDI-Verlag, Düsseldorf, Germany, 1991.
  2. J. Jacob and K. Petrick, “Qualitätsmanagement und Normung,” in Masing Handbuch Qualitätsmanagement, R. Schmitt and T. Pfeifer, Eds., pp. 101–121, Carl Hanser Verlag, München, Germany, 2007. View at Google Scholar
  3. M. Hoffmann, T. Goesmann, and A. Kienle, “Analyse und Unterstützung von Wissensprozessen als Voraussetzung für erfolgreiches Wissensmanagement,” in Geschäftsprozessorientiertes Wissensmanagement, A. Abecker, K. Hinkelmann, H. Maus, and H. J. Müller, Eds., pp. 159–181, Springer, Berlin, Germany, 2002. View at Google Scholar
  4. S. Kumar, Intelligent Manufacturing Systems, B.I.T. Mesra, Ranchi, India, 2002, http://pchats.tripod.com/int_manu.pdf.
  5. S. Kalpakjian and S. R. Schmid, Manufacturing Engineering and Technology, Prentice Hall, New Jersey, NJ, USA, 2009.
  6. J. Sölter, “Relationship between strain distributions and shape deviations of rings caused in clamping,” Materialwissenschaft und Werkstofftechnik, vol. 43, no. 1-2, pp. 23–28, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. T. Becker, Prozesse in Produktion und Supply Chain Optimieren, Springer, Berlin, Germany, 2nd edition, 2008.
  8. K. Yang and J. Trewn, Multivariate Statistical Methods in Quality Management, McGraw-Hill, New York, NY, USA, 2004.
  9. G. Köksal, İ. Batmaz, and M. C. Testik, “A review of data mining applications for quality improvement in manufacturing industry,” Expert Systems with Applications, vol. 38, no. 10, pp. 13448–13467, 2011. View at Publisher · View at Google Scholar
  10. H. A. Simon, “Why should machines learn?” in Machine Learning: An Artificial Intelligence Approach, R. Michalski, J. Carbonell, and T. Mitchell, Eds., pp. 25–38, Tioga Press, Charlotte, NC, USA, 1983. View at Google Scholar
  11. S. C.-Y. Lu, “Machine learning approaches to knowledge synthesis and integration tasks for advanced engineering automation,” Computers in Industry, vol. 15, no. 1-2, pp. 105–120, 1990. View at Publisher · View at Google Scholar · View at Scopus
  12. L. Monostori, “AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing,” Engineering Applications of Artificial Intelligence, vol. 16, no. 4, pp. 277–291, 2003. View at Publisher · View at Google Scholar · View at Scopus
  13. S. B. Kotsiantis, “Supervised machine learning: a review of classification techniques,” Informatica, vol. 31, no. 3, pp. 249–268, 2007. View at Google Scholar · View at MathSciNet
  14. C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Burbidge, M. Trotter, B. Buxton, and S. Holden, “Drug design by machine learning: support vector machines for pharmaceutical data analysis,” Computers and Chemistry, vol. 26, no. 1, pp. 5–14, 2001. View at Publisher · View at Google Scholar · View at Scopus
  16. V. Cherkassky and Y. Ma, “Another look at statistical learning theory and regularization,” Neural Networks, vol. 22, no. 7, pp. 958–969, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. S. S. Keerthi and C.-J. Lin, “Asymptotic behaviors of support vector machines with gaussian kernel,” Neural Computation, vol. 15, no. 7, pp. 1667–1689, 2003. View at Publisher · View at Google Scholar · View at Scopus
  18. R. Khemchandani, Jayadeva, and S. Chandra, “Knowledge based proximal support vector machines,” European Journal of Operational Research, vol. 195, no. 3, pp. 914–923, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. K. Salahshoor, M. Kordestani, and M. S. Khoshro, “Fault detection and diagnosis of an industrial steam turbine using fusion of SVM (support vector machine) and ANFIS (adaptive neuro-fuzzy inference system) classifiers,” Energy, vol. 35, no. 12, pp. 5472–5482, 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. R. B. Chinnam, “Support vector machines for recognizing shifts in correlated and other manufacturing processes,” International Journal of Production Research, vol. 40, no. 17, pp. 4449–4466, 2002. View at Publisher · View at Google Scholar · View at Scopus
  21. A. Widodo and B.-S. Yang, “Support vector machine in machine condition monitoring and fault diagnosis,” Mechanical Systems and Signal Processing, vol. 21, no. 6, pp. 2560–2574, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. J. Sun, M. Rahman, Y. S. Wong, and G. S. Hong, “Multiclassification of tool wear with support vector machine by manufacturing loss consideration,” International Journal of Machine Tools and Manufacture, vol. 44, no. 11, pp. 1179–1187, 2004. View at Publisher · View at Google Scholar · View at Scopus
  23. A. Ben-Hur and J. Weston, “A user’s guide to support vector machines,” in Data Mining Techniques for the Life Sciences, O. Carugo and F. Eisenhaber, Eds., vol. 609 of Methods in Molecular Biology, pp. 223–239, Humana Press, Totowa, NJ, USA, 2010. View at Publisher · View at Google Scholar
  24. Q. Wu, “Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system,” Journal of Computational and Applied Mathematics, vol. 233, no. 10, pp. 2481–2491, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  25. A. Azadeh, M. Saberi, A. Kazem, V. Ebrahimipour, A. Nourmohammadzadeh, and Z. Saberi, “A flexible algorithm for fault diagnosis in a centrifugal pump with corrupted data and noise based on ANN and support vector machine with hyper-parameters optimization,” Applied Soft Computing, vol. 13, no. 3, pp. 1478–1485, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. T. Wuest, C. Irgens, and K.-D. Thoben, “An approach to monitoring quality in manufacturing using supervised machine learning on product state data,” Journal of Intelligent Manufacturing, vol. 25, no. 5, pp. 1167–1180, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, no. 1–3, pp. 389–422, 2002. View at Publisher · View at Google Scholar · View at Scopus
  28. Y. Chang and C. Lin, “Feature ranking using linear SVM,” JMLR: Workshop and Conference Proceedings, vol. 3, pp. 53–64, 2008. View at Google Scholar
  29. M. Mccann, Y. Li, L. Maquire, and A. Johnston, “Causality Challenge: benchmarking relevant signal components for effective monitoring and process control,” Journal of Machine Learning Research: Workshop and Conference Proceedings, vol. 6, pp. 277–288, 2010. View at Google Scholar
  30. M. Kuhn and K. Johnson, Applied Predictive Modeling, Springer, New York, NY, USA, 2013.
  31. J. Sölter, “Modeling and simulation of ring deformation due to clamping,” Materialwissenschaft und Werkstofftechnik, vol. 40, no. 5-6, pp. 380–384, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. Y. S. Abu-Mostafa and A. F. Atiya, “Introduction to financial forecasting,” Applied Intelligence, vol. 6, no. 3, pp. 205–213, 1996. View at Publisher · View at Google Scholar · View at Scopus
  33. J. W. Hall, “Adaptive selection of US stocks with neural nets,” in Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets, G. J. Deboeck, Ed., pp. 45–65, Wiley, New York, NY, USA, 1994. View at Google Scholar
  34. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: synthetic minority over-sampling technique,” Journal of Artificial Intelligence Research, vol. 16, pp. 321–357, 2002. View at Google Scholar · View at Scopus