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Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic.

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This study introduces a method to identify manufacturing quality control issues even with uncertain abnormal patterns on control charts. It quantifies pattern occurrence and uses fuzzy logic to rank potential causes for defect elimination.

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Area of Science:

  • Industrial Engineering
  • Quality Control
  • Artificial Intelligence

Background:

  • Control charts are vital for manufacturing quality control.
  • Identifying abnormal patterns helps locate assignable causes of defects.
  • Uncertainty in pattern recognition hinders effective diagnosis.

Purpose of the Study:

  • To develop a method for quantifying abnormal control chart pattern occurrence under uncertainty.
  • To create a fuzzy logic system for calculating the contribution of assignable causes based on fuzzy abnormal patterns.
  • To support defect elimination in manufacturing processes.

Main Methods:

  • A characteristic numbers based recognition method to quantify the degree of abnormal pattern occurrence.
  • A fuzzy inference system utilizing fuzzy logic to determine the contribution of assignable causes.
  • Analysis of four common abnormal control chart patterns.

Main Results:

  • The proposed method successfully quantifies the occurrence degree of abnormal patterns despite uncertainties.
  • The fuzzy inference system effectively calculates the contribution of assignable causes.
  • A ranked list of potential causes for abnormalities was generated.

Conclusions:

  • The developed approach provides a robust solution for diagnosing issues under fuzzy control chart abnormal patterns.
  • This method aids in identifying and eliminating manufacturing defects by ranking assignable causes.
  • It enhances the effectiveness of quality control systems in uncertain environments.