Advertisement

Interfacing Quantitative NDE with Computer Algorithms for Automated Statistical Process Control

  • E. P. Papadakis

Abstract

In the Factory of the Future (FOF), production will be unified under a system of Computer Integrated Manufacturing (CIM). Automatic computer-integrated control of processes, detection of errors, and determination of corrective action will be necessary because of the proposed level of manpower in the FOFL[1,2]. Under these circumstances, a process which might go out of control and remain that way would be highly detrimental. Very rapid Statistical Process Control (SPC) will provide definitive warnings of out-of-control conditions.

Keywords

Monte Carlo Computer Simulation Control Chart Stical Process Control Computer Integrate Manufacture Western Electric 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J. Tukloff, Industrial Engineering 16 (2), 46–52 (1984).Google Scholar
  2. 2.
    M. E. Merchant, IEEE Spectrum 20 (5), 36–39 (1983).CrossRefGoogle Scholar
  3. 3.
    Western Electric Company (1956), Statistical Quality Control Handbook (Western Electric Co., Newark, New Jersey), pp. 24–28 et al.Google Scholar
  4. 4.
    Western Electric Company (1956), Statistical Quality Control Handbook (Western Electric Co., Newark, New Jersey), pp. 180–183.Google Scholar
  5. 5.
    Advanced Systems and Designs, Inc., Dearborn, Michigan.Google Scholar
  6. 6.
    BBN Software Products, Northbrook, Illinois.Google Scholar
  7. 7.
    Perceptron, Inc., Farmington Hills, Michigan.Google Scholar
  8. 8.
    K. J. Law Engineerings, Inc., Farmington Hills, Michigan.Google Scholar

Copyright information

© Springer Science+Business Media New York 1989

Authors and Affiliations

  • E. P. Papadakis
    • 1
  1. 1.Center for NDEIowa State UniversityAmesUSA

Personalised recommendations