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Abstract:
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One of the critical steps during the planning of a mixture experiment is the selection of the component lower and upper bounds. Ideally, any optimum should occur near the center of the experimental region, where prediction precision is high. In practice, however, at least one of these bounds is often chosen to be too narrow, causing an optimum occur along an edge or vertex of the experiment-design space. Experimenters will often simply re-run an entirely new experiment with wider ranges due to lack of readily-available augmentation strategies. Augmentation and sequential strategies for response surface methodology (RSM) have been typically studied in the context of model order or sample size. However, strategies for the expansion of the region for experimentation are sparse, especially for mixture designs. In this talk, we briefly describe the general problem. Then we pay particular attention to mixture experiments, where expanding component ranges is complicated due to their interdependence on one another. We propose several strategies, including an optimal DOE space augmentation algorithm. Several examples will be given.
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