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데이터 마이닝을 이용한 로버스트 설계 모형의 최적화

Optimization of Robust Design Model using Data Mining

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참고문헌

주제분류산업공학

초록

Abstract

According to the automated manufacturing processes followed by the development of computer manufacturing technologies, products or quality characteristics produced on the processes have measured and recorded automatically. Much amount of data daily produced on the processes may not be efficiently analyzed by current statistical methodologies (i.e., statistical quality control and statistical process control methodologies) because of the dimensionality associated with many input and response variables. Although a number of statistical methods to handle this situation, there is room for improvement. In order to overcome this limitation, we integrated data mining and robust design approach in this research. We find efficiently the significant input variables that connected with the interesting response variables by using the data mining technique. And we find the optimum operating condition of process by using RSM and robust design approach.

참고문헌 (20)

  1. 김만선, "신경망을 이용한 대용량 데이터 처리를 위한 군집화 방법", 10 (2002)
  2. 이현우, "A Study on Data Mining Application Problem in the TFT-LCD Industry", 16(4), pp.91~101 (2005)
  3. 이기훈, "데이터 마이닝에서 로버스트 통계적 기법의 도입", 18 (2000)
  4. Besharati, "Multi-Objective Single Product Robust Optimization: An Integrated Design and Marketing Approach", 128(4), pp.884~892 (2006)
  5. Chang X. F, "Data mining techniques applied to predictive modeling of the knurling process", 36, pp.253~263 (2004)
  6. Cho D. H, "Prioritization of association rules in data mining : Multiple criteria decision approach", 29(4), pp.867~878 (2005)
  7. Cho, "Optimization Issues in Quality Engineering" (1994)
  8. DuMonuchel, "Bayesian Data Mining in Large Frequency Tables With an Application to the Spontaneous Reportign System", 53, pp.177~202 (1999)
  9. Gardner, "Data Mining Solves Though Semiconductor Manufacturing Problem Conference on Knowledge Discovery in Data Proceedings of the sixth ACMSIGKDD international conference on Knowledge discovery and data mining", pp.376~383 (2000)
  10. Hall, "Correlation-based Feature Selection for Machine Learning" (1998)
  11. John, "” In Machine Learning Proceedings of the Eleventh International Conference" (1994), Morgan Kaufmann
  12. Lin, "Dual response surface optimation", 27, pp.34~39 (1995)
  13. Montgomery D. C, "Introduction to Statistical Quality Control 4th edn." (2001), John Wiley & Sons
  14. and Ramberg, "“Discussion of off-line quality control ” Journal of Quality Technology", pp.151~161 (1985)
  15. Seifert, "Data Mining: An Overview" (2004)
  16. Shin, "Bias-specified robust design optimization and its analytical solutions", 48, pp.129~140 (2005)
  17. Fayyad, "The KDD Process for Extracting Useful Knowledge from Volumes of Data", 39(11), pp.27~34 (1996)
  18. Vining, "” Journal of Quality Technology", pp.38~45 (1990)
  19. Witten, "Data Mining: Practical Machines Learning Tools and Techniques 2nd edn Morgan Kaufmann" (2005)
  20. Yu, "Feature Selection for High-Dimensional Data : A Fast Correlation-Based Filter Solution ; The Proceedings of the 20th International Conference on Machine Learning (ICML-03)", pp.856~863 (2003)

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