JSM 2011 Online Program

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Abstract Details

Activity Number: 30
Type: Contributed
Date/Time: Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
Sponsor: Section on Quality and Productivity
Abstract - #300682
Title: Supersaturated Designs for Robust Products and Processes
Author(s): Chris Marley*+ and David Woods and Dennis Lin
Companies: University of Southampton and Southampton Statistical Sciences Research Institute and Penn State University
Address: , , , UK
Keywords: Control factor ; $E(s^2)$-optimality ; Noise factor ; Non-regular design ; Screening experiment
Abstract:

In industrial experimentation a key tool for improving quality of products and processes is the exploitation of interactions between control factors (which can be set in the product specification) and noise factors (which cannot). Such interactions can be exploited to find settings of the control factors that dampen the variability in the response due to variability in the noise factors. One problem with such experiments is that, even for a moderate number of control and noise factors, a large number of interactions will be of interest. Traditional methods of planning experiments result in designs with many runs, which will be costly if each run is expensive to perform. In such a situation, an alternative is a supersaturated design, in which the number of collected observations is less than the number of factorial effects requiring estimation. We introduce a new class of supersaturated designs with both control and noise factors, and demonstrate their potential in terms of cost savings and flexibility. Examples are presented to illustrate, in particular, how the new designs have a higher power to detect those effects of interest that have a substantive impact on the response.


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