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Control charts using half-normal and half-exponential power distributions using repetitive sampling.

Muhammad Naveed1,2, Muhammad Azam3, Nasrullah Khan4

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This study introduces new attribute control charts (ACC) for monitoring manufacturing defects using time-truncated life tests. The half-exponential power distribution (HEPD) based chart demonstrates superior performance in detecting process shifts.

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Area of Science:

  • Industrial Engineering
  • Statistical Quality Control
  • Reliability Engineering

Background:

  • Manufacturing processes require robust methods for monitoring defective items.
  • Traditional control charts may not fully capture lifetime data characteristics.
  • Time-truncated life tests offer a framework for analyzing data with limited observation periods.

Purpose of the Study:

  • To develop and evaluate attribute control charts (ACC) for monitoring defective items in manufacturing.
  • To tailor these charts using time-truncated life tests (TTLT) for specific lifetime distributions.
  • To assess the performance of proposed charts under repetitive sampling schemes (RSS).

Main Methods:

  • Development of ACCs based on the half-normal distribution (HND) and half-exponential power distribution (HEPD).
  • Application of time-truncated life tests (TTLT) and repetitive sampling schemes (RSS).
  • Performance evaluation using average run length (ARL) calculations for in-control and out-of-control scenarios, considering parameter shifts.

Main Results:

  • The HEPD-based ACC significantly outperforms HND-based and Exponential distribution (ED)-based ACCs in detecting process shifts, evidenced by lower ARL values.
  • The HND-based ACC also shows improved effectiveness compared to the ED-based ACC under TTLT and RSS.
  • Simulation testing and real-life implementation confirm the practical applicability of the proposed control charts.

Conclusions:

  • The proposed HEPD-based attribute control chart is highly effective for monitoring manufacturing quality.
  • The HND-based ACC offers a valuable alternative for quality control applications.
  • These advanced control charts enhance defect detection and process monitoring in manufacturing settings.