Open Access
Issue
MATEC Web Conf.
Volume 123, 2017
2017 The 2nd International Conference on Precision Machinery and Manufacturing Technology (ICPMMT 2017)
Article Number 00029
Number of page(s) 6
DOI https://doi.org/10.1051/matecconf/201712300029
Published online 21 September 2017
  1. Y. Qi, P. Wang, and X. Gao. Enhanced batch process monitoring and quality prediction using multi-phase dynamic PLS, in Control Conference (CCC), 2011 30th Chinese. 2011. IEEE. [Google Scholar]
  2. C. Zhao et al. Stage-based soft-transition multiple PCA modeling and on-line monitoring strategy for batch processes Journal of Process Control 179 2007 728–741 [CrossRef] [Google Scholar]
  3. M. McCann et al. Causality Challenge: Benchmarking relevant signal components for effective monitoring and process control in NIPS Causality: Objectives and Assessment 2010 [Google Scholar]
  4. K. Kerdprasop, N. Kerdprasop Feature selection and boosting techniques to improve fault detection accuracy in the semiconductor manufacturing process in Proceedings of the International MultiConference of Engineers and Computer Scientist. 2011 [Google Scholar]
  5. S. Munirathinam, B. Ramadoss Predictive Models for Equipment Fault Detection in the Semiconductor Manufacturing Process International Journal of Engineering and Technology 84 2016 p273 [CrossRef] [Google Scholar]
  6. D. Djurdjanovic, J. Ni Stream of variation based analysis and synthesis of measurement schemes in multi-station machining systems, Ann Arbor 1001 2001 2109–2125 [Google Scholar]
  7. F. Arif, N. Suryana, B. Hussin A data mining approach for developing quality prediction model in multi-stage manufacturing International Journal of Computer Applications 6922. 2013 [Google Scholar]
  8. P. Jiang et al. Real-time quality monitoring and predicting model based on error propagation networks for multistage machining processes Journal of Intelligent Manufacturing 253 2014 521–538 [CrossRef] [Google Scholar]
  9. K. Chomboon, K. Kerdprasop, N. Kerdprasop Rare class discovery techniques for highly imbalance data in Proc International multi conference of engineers and computer scientists. 2013 [Google Scholar]
  10. K. Salahshoor, H.K. Alaei, and H.K. Alaei. A new on-line predictive monitoring using an integrated approach adaptive filter and PCA, in Soft Computing Applications (SOFA), 2010 4th International Workshop on. 2010. IEEE. [Google Scholar]
  11. R. Agrawal, R. Srikant Fast algorithms for mining association rules in Proc. 20th int. conf. very large data bases, VLDB 1994 [Google Scholar]
  12. H. Huang, G. Quan, and J. Fan, Leakage temperature dependency modeling in system level analysis, in Quality Electronic Design (ISQED), 2010 11th International Symposium on. 2010. IEEE. [Google Scholar]
  13. K. Friston et al. Functional connectivity: the principal-component analysis of large (PET) data sets Journal of Cerebral Blood Flow & Metabolism 131 1993 5–14 [CrossRef] [EDP Sciences] [Google Scholar]
  14. H. Wold Partial least squares Encyclopedia of statistical sciences 1985 [Google Scholar]
  15. P. Murphy, D. Aha, UCIML repository secom dataset. [Google Scholar]
  16. G.S. May, C.J. Spanos Fundamentals of semiconductor manufacturing and process control. 2006 John Wiley & Sons [CrossRef] [Google Scholar]

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