Generally weighted moving average control chart in the presence of measurement error via auxiliary information utilization
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an improved Auxiliary Information Based GWMA with Measurement Error (AIB-GWMA-ME) chart. The new chart enhances manufacturing process monitoring by effectively detecting small shifts despite measurement errors.
Area Of Science
- Industrial Engineering
- Statistical Quality Control
- Manufacturing Systems
Background
- Control charts are vital for monitoring manufacturing process stability.
- Measurement error can significantly impair the effectiveness of traditional control charts in detecting process shifts.
- Existing methods struggle to maintain high sensitivity in the presence of measurement error.
Purpose Of The Study
- To introduce an improved Generally Weighted Moving Average (GWMA) chart designed to mitigate the impact of measurement error.
- To develop the Auxiliary Information Based GWMA with Measurement Error (AIB-GWMA-ME) chart.
- To enhance the accuracy and sensitivity of process monitoring in manufacturing settings with measurement error.
Main Methods
- The study developed the AIB-GWMA-ME chart statistic by integrating auxiliary information with a measurement error adjustment mechanism.
- Three distinct measurement error models were considered: covariate, multiple measurements, and linearly increasing variance.
- Control limits for the AIB-GWMA-ME chart were determined for each model.
- Monte Carlo simulations were employed to evaluate the chart's performance using Average Run Length (ARL) metrics.
Main Results
- The AIB-GWMA-ME chart demonstrated improved sensitivity in detecting small process shifts compared to existing charts.
- Performance evaluations using Average Run Length (ARL) indicated superior effectiveness in the presence of measurement error.
- The chart's efficacy was validated across three different measurement error models.
Conclusions
- The AIB-GWMA-ME chart offers a significant advancement in statistical process control when measurement error is present.
- This improved chart enhances the ability to maintain manufacturing process stability and quality.
- The AIB-GWMA-ME chart provides a more robust solution for process monitoring than traditional GWMA and EWMA charts under measurement error conditions.
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