Journal of Intelligent Manufacturing

, Volume 14, Issue 3, pp 379–388

Identification of process disturbance using SPC/EPC and neural networks

  • Chih-Chou Chiu
  • Yuehjen E. Shao
  • Tian-Shyug Lee
  • Ker-Ming Lee
Article

DOI: 10.1023/A:1024657911399

Cite this article as:
Chiu, CC., Shao, Y.E., Lee, TS. et al. Journal of Intelligent Manufacturing (2003) 14: 379. doi:10.1023/A:1024657911399

Abstract

Since solely using statistical process control (SPC) and engineering process control (EPC) cannot optimally control the manufacturing process, lots of studies have been devoted to the integrated use of SPC and EPC. The majority of these studies have reported that the integrated approach has better performance than that by using only SPC or EPC. Almost all these studies have assumed that the assignable causes of process disturbance can be identified and removed by SPC techniques. However, these techniques are typically time-consuming and thus make the search hard to implement in practice. In this paper, the EPC and neural network scheme were integrated in identifying the assignable causes of the underlying disturbance. For finding the appropriate setup of the networks' parameters, such as the number of hidden nodes and the suitable input variables, the all-possible-regression selection procedure is applied. For comparison, two SPC charts, Shewhart and cumulative sum (Cusum) charts were also developed for the same data sets. As the results reveal, the proposed approaches outperform the other methods and the shift of disturbance can be identified successfully.

Statistical process control engineering process control minimum mean squared error control neural networks identification of process disturbance 

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Chih-Chou Chiu
    • 1
  • Yuehjen E. Shao
    • 2
  • Tian-Shyug Lee
    • 3
  • Ker-Ming Lee
    • 4
  1. 1.Institute of Commerce Automation and ManagementNational Taipei University of TechnologyTaipeiTaiwan, R.O.C
  2. 2.Department of Statistics and Information SciencesFu-Jen Catholic University TaipeiTaiwan, R.O.C
  3. 3.Department of Business AdministrationFu-Jen Catholic University TaipeiTaiwan, R.O.C
  4. 4.Institute of Applied StatisticsFu-Jen Catholic University TaipeiTaiwan, R.O.C

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