SPC Monitoring of MMSE- and Pi-Controlled Processes

To reduce variation in manufacturing processes, traditional statistical process control (SPC) techniques can be applied to monitor automatic process control (APC) controlled processes for detecting assignable cause process variation. In this paper we compare the monitoring of process output and the...

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Bibliographic Details
Published in:Journal of quality technology 2002-10, Vol.34 (4), p.384-398
Main Authors: Jiang, Wei, Tsui, Kwok-Leung
Format: Article
Language:English
Subjects:
Autoregressive Moving Average Process
Comparative analysis
Control charts
Controllers
Noise
Process controls
Quality Control
Ratios
Regression analysis
Statistical Process Control
Studies
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Summary:To reduce variation in manufacturing processes, traditional statistical process control (SPC) techniques can be applied to monitor automatic process control (APC) controlled processes for detecting assignable cause process variation. In this paper we compare the monitoring of process output and the monitoring of the control action of Minimum-Mean-Squared-Error- and Proportional-Integral-Controlled processes. We show that the robustness property of the PI controller makes it difficult to detect unanticipated mean shifts when the process output is being monitored. We illustrate how the signal-to-noise ratios developed in Jiang, Tsui, and Woodall (2000) can be used to predict the SPC chart performance and help select the appropriate chart for monitoring.
ISSN:0022-4065
2575-6230
DOI:10.1080/00224065.2002.11980171