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Abstract:
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The method of Operating Window (OW) is a statistical-engineering approach to improve the robustness and reliability of products/processes. Existing research on OW has paid little attention to this aspect of the OW experiments. In this paper we focus on the modeling and optimization of OW experiments by incorporating the split-plot structure. For ease of reading, we use the ubiquitous paper feeder example to illustrate each step of modeling and optimization. First, we employ the generalized linear mixed effects models to model the complex error structure afforded by the split-plot structure. Then we obtain statistically significant variables for each failure mode in the feeder example: misfeed and multifeed. These analysis results enable us to make inference about the predicted failure probability for each mode. The optimization step is performed by minimizing some performance measures proposed in Joseph and Wu (2002). Performance measures for each control run are calculated first and then modeled in terms of identified control variables. The case study not only reveals the split-plot structure and identify the key factors for the process, but provides some additional insights.
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