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Related Experiment Video

Updated: Sep 8, 2025

Operation of the Collaborative Composite Manufacturing CCM System
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Fuzzy process capability indices for simple linear profile.

Zainab Abbasi Ganji1, Bahram Sadeghpour Gildeh2

  • 1Khorasan Razavi Agricultural and Natural Resources Research and Education Center, AREEO, Mashhad, Iran.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces two fuzzy logic methods to measure process capability when specification limits are imprecise. These methods help assess process performance with uncertain data.

Keywords:
Simple linear profilefuzzy logicprocess capability indexranking functiontriangular fuzzy numbers

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Area of Science:

  • Industrial Engineering
  • Statistical Quality Control
  • Fuzzy Logic Systems

Background:

  • Process capability indices (PCIs) are crucial for evaluating manufacturing process performance against specifications.
  • Traditional PCIs struggle with imprecise, incomplete, or uncertain data, common in real-world scenarios.
  • Fuzzy logic offers a robust framework for handling such data uncertainties.

Purpose of the Study:

  • To develop novel fuzzy methods for assessing process capability in simple linear profiles.
  • To address situations where lower and upper specification limits are imprecise or vague.
  • To provide practical guidance for applying these fuzzy methods in quality control.

Main Methods:

  • Development of two distinct fuzzy logic-based methods for process capability assessment.
  • Application of fuzzy set theory to handle imprecise lower and upper specification limits.
  • Integration of fuzzy logic with statistical process control concepts for linear profiles.

Main Results:

  • The proposed fuzzy methods effectively quantify process capability even with imprecise specification limits.
  • Demonstration of the methods' utility through a numerical example, aiding practitioner understanding.
  • The fuzzy approach provides a more realistic assessment of process performance under uncertainty.

Conclusions:

  • Fuzzy logic methods offer a valuable extension to traditional process capability analysis, especially for imprecise data.
  • These methods enhance the ability to manage and improve processes with uncertain specifications.
  • The study provides a practical tool for quality engineers dealing with real-world data complexities.