Skip to main content

An Adaptive Sampling System with a Fuzzy Controller

  • Conference paper
  • 397 Accesses

Part of the book series: Frontiers in Statistical Quality Control ((FSQC,volume 5))

Abstract

The main intentions that we use sampling systems are ➀ to assure required quality to post-process and ➁ to make suppliers (pre-process) manufacture products of required quality, giving them information about the results of inspection. Sampling systems such as ISO 3951 [1], ISO 2859 [2], etc., specify the AQLs (Acceptable Quality Levels) which should be the upper limit of process average, as well as the switching rules which inform the suppliers about quality level in order to meet the required quality. KOYAMA [3] has, however, pointed out poor sensitivity to process changes, as a weak performance of these sampling systems with the switching rules.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. ISO 3951 (1989/1979): Sampling Procedures and Charts for Inspection by Variables for Percent Nonconforming, International Organization for Standardization.

    Google Scholar 

  2. ISO 2859 (1974): Sampling Procedures and Tables for Inspection by Attributes, International Organization for Standardization.

    Google Scholar 

  3. KOYAMA, T.(1981): Average Outgoing Quality Through Sampling Systems, Frontiers in Statistical Quality Control (Lenz, H. -J., et al. ed., Physica-Verlag), p. 113–132.

    Google Scholar 

  4. ISO/CD 2859–1 (1994): Sampling Procedures for Inspection by Attributes, Part I: Sampling Plans Indexed by Acceptance Quality Level (AQL) for Lot-by-lot Inspection, International Organization for Standardization.

    Google Scholar 

  5. KOYAMA, T.(1994): Effective Quality Assurance with Technological Intelligence, REA J, Vol. 15, No. 4, p. 3–10 (in Japanese).

    MathSciNet  Google Scholar 

  6. MAMDANI, E. H.(1974): Application of Fuzzy Algorithms for Control of Simple Dynamic Plant, Proc. IEEE, Vol. 121, No. 12, p. 1585–1588.

    Google Scholar 

  7. MOOD, A. M.(1943): On the Dependence of Sampling Inspection Plans upon Population Distributions, Ann. Math. Statist, Vol. 14, p. 415–425.

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Koyama, T. (1997). An Adaptive Sampling System with a Fuzzy Controller. In: Lenz, HJ., Wilrich, PT. (eds) Frontiers in Statistical Quality Control. Frontiers in Statistical Quality Control, vol 5. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-59239-3_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-59239-3_1

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-0984-8

  • Online ISBN: 978-3-642-59239-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics