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Optimal estimated process parameters side sensitive group runs chart based on expected average run length.

Huay Woon You1

  • 1Pusat PERMATApintar Negara, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.

Heliyon
|November 3, 2018
PubMed
Summary
This summary is machine-generated.

The side sensitive group runs (SSGR) chart effectively detects process shifts, especially when parameters are estimated. This study optimizes the SSGR chart using expected average run length for better real-world performance.

Keywords:
Applied mathematicsIndustry

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Area of Science:

  • Statistical Process Control
  • Quality Engineering
  • Industrial Statistics

Background:

  • The side sensitive group runs (SSGR) chart demonstrates superior performance over Shewhart and synthetic charts for detecting small to moderate process mean shifts.
  • Estimating process parameters from Phase-I samples is crucial for practical SSGR chart implementation, though it requires substantial data for optimal performance.
  • Traditional performance evaluation using Average Run Length (ARL) assumes known shift sizes, which is often unrealistic in practice.

Purpose of the Study:

  • To propose an optimal design for the SSGR chart with estimated process parameters.
  • To evaluate the SSGR chart's performance using Expected Average Run Length (EARL) and Standard Deviation of ARL (SDARL), accounting for unknown shift sizes and practitioner variability.
  • To provide practical guidance for implementing an optimized SSGR chart in manufacturing settings.

Main Methods:

  • Performance evaluation of the SSGR chart using EARL and SDARL metrics.
  • Development of an optimal design criterion for the SSGR chart based on EARL.
  • Application and demonstration using real-world manufacturing data.

Main Results:

  • The study establishes an optimal design for the SSGR chart with estimated parameters, improving its sensitivity and reliability.
  • EARL and SDARL provide robust performance measures for SSGR charts when process parameters and shift sizes are unknown.
  • The optimized SSGR chart demonstrates practical utility in a manufacturing context.

Conclusions:

  • The proposed optimal design enhances the SSGR chart's effectiveness in real-world industrial applications with estimated parameters.
  • The use of EARL and SDARL is recommended for a more accurate assessment of control chart performance under uncertainty.
  • The study contributes to the advancement of statistical process control methodologies for quality improvement.