Hybrid neuro-fuzzy approach to the generation of measuring points for knowledge-based inspection planning

Inshik Hwang, Hong Chul Lee, Sungdo Ha

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

A knowledge-based inspection planning system is presented that can generate effective and consistent inspection plans automatically. The knowledge-based inspection planning system integrates part geometry information, tolerance information and heuristic knowledge of experienced inspection planners to determine the numbers and positions of measurement points. The system receives the tolerance information from users and stores it in the common database with 3D CAD geometry. A set of fuzzy rules and membership functions is automatically extracted from historic learning data using a hybrid neuro-fuzzy method. After the fuzzy rules are generated by the hybrid neuro-fuzzy model, a genetic algorithm is applied to optimize the weight parameters to find the best values for the constants. The proposed knowledge-based inspection planning system provides the stable and consistent inspection plan by removing the subjectivity of a human planner.

Original languageEnglish
Pages (from-to)2507-2520
Number of pages14
JournalInternational Journal of Production Research
Volume40
Issue number11
DOIs
Publication statusPublished - 2002 Jul 20

Fingerprint

Inspection
Planning
Fuzzy rules
Geometry
Membership functions
Set theory
Knowledge-based
Neuro-fuzzy
Computer aided design
Genetic algorithms
Tolerance

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Cite this

Hybrid neuro-fuzzy approach to the generation of measuring points for knowledge-based inspection planning. / Hwang, Inshik; Lee, Hong Chul; Ha, Sungdo.

In: International Journal of Production Research, Vol. 40, No. 11, 20.07.2002, p. 2507-2520.

Research output: Contribution to journalArticle

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