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Surrogate Model Development for Digital Experiments in Welding

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New adaptive EWMA CV control chart with application to the sintering process.

Sadaf Ayesha¹, Asma Arshad¹, Olayan Albalawi²

¹Department of Statistics, National College of Business Administration and Economics, Lahore, Pakistan.

Scientific Reports

|May 21, 2024

Summary

This study introduces an adaptive exponentially weighted moving average (EWMA) control chart for monitoring coefficient of variation (CV). The new AAEWMA CV chart effectively detects infrequent CV changes in unstable production processes.

Keywords:

Adaptive control charts Average run length Coefficient of variation EWMA Standard deviation run length Statistical process control

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Area of Science:

Industrial Engineering
Statistical Quality Control
Process Monitoring

Background:

Monitoring relative process variability using the coefficient of variation (CV) is crucial for long-term production observations, especially with unstable means.
Existing exponentially weighted moving average (EWMA) charts may lack sensitivity in detecting infrequent changes in process CV.
Adaptive control charts offer potential improvements in detecting process shifts.

Purpose of the Study:

To develop and evaluate a novel modified adaptive exponentially weighted moving average (AAEWMA) control chart for coefficient of variation (CV) monitoring.
To enhance the detection of infrequent process CV changes in industrial settings.
To improve upon the effectiveness of existing adaptive EWMA CV charts.

Main Methods:

Development of a new adaptive function to adjust the smoothing constant based on estimated CV shift sizes.
Utilizing the Monte Carlo simulation method to compute run-length values for efficiency analysis.
Application of an industrial data example to demonstrate the chart's practical implementation and effectiveness.

Main Results:

The proposed AAEWMA CV control chart demonstrates superior efficiency compared to the existing AEWMA CV chart.
The adaptive function effectively adjusts the smoothing constant, improving the detection of infrequent CV shifts.
Simulation results confirm the enhanced performance of the AAEWMA CV chart in identifying process variability changes.

Conclusions:

The novel AAEWMA CV control chart is highly effective in monitoring relative process variability and detecting infrequent CV changes.
The adaptive mechanism significantly improves the sensitivity and performance of EWMA charts for CV monitoring.
Implementation of the proposed AAEWMA CV chart is strongly recommended for industrial quality control.