Title page for ETD etd-06192006-125721
|Type of Document
||Kim, Yoon G.
||A response surface approach to data analysis in robust parameter design
|Myers, Raymond H.
|Hinkelmann, Klaus H.
|Holtzman, Golde I.
|Krutchkoff, Richard G.
|Pirie, Walter R.
|Date of Defense
It has become obvious that combined arrays and a response surface
approach can be effective tools in our quest to reduce (process) variability. An
important aspect of the improvement of quality is to suppress the magnitude of
the influence coming from subtle changes of noise factors. To model and control
process variability induced by noise factors we take a response surface approach.
The derivative of the standard response function with respect to noise factors,
i. e., the slopes of the response function in the direction of the noise factors, play
an important role in the study of the minimum process variance. For better
understanding of the process variability, we study various properties of both
biased and the unbiased estimators of the process variance. Response surface
modeling techniques and the ideas involved with variance modeling and
estimation through the function of the aforementioned derivatives is a valuable
concept in this study. In what follows, we describe the use of the response surface
methodology for situations in which noise factors are used. The approach is to
combine Taguchi's notion of heterogeneous variability with standard design and
modeling techniques available in response surface methodology.
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