Optimal selection of blocked robust parameter designs and their applications
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Blocking is a useful technique to control systematic variation in experiments. Robust parameter design is widely used as an effective tool to reduce process variability by appropriate selection of control factors to make the process insensitive to noise. In this paper, we propose and study a method for selecting the optimal blocked robust parameter designs when some of the control-by-noise interactions are included in the model. We then discuss how to search for the best designs according to this method and present some results for designs of 8 and 16 runs.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.