Abstract
Industrial experimentation now recognizes that for a characteristic of interest, the process variability is just as important as the process mean. The Japanese industrial engineer, Taguchi (see Taguchi and Wu [9]), pioneered this area through his robust parameter design. Much of the recent work in robust parameter design has focused on the combined array (Welch et. al [10], Shoemaker et al. [7], Box and Jones [1]). Robust parameter design assumes that the experimental factors separate into two classes: control and noise. Control factors are those under the direct control of the experimenter both in the experiment and in the process. Noise factors are those which for some reason or another are not controllable in the process, and thus are random in the process, although they are to some extent under the experimenter’s control in the actual experiment. The combined array runs a single experiment in the control and noise factors, treating the noise factors as fixed effects, and allows the analyst to employ modifications of traditional response surface methodology. Myers et. al [4] show how to construct separate response surfaces for the process mean and the process variance. These surfaces can be used to determine optimum operating conditions in terms of the control factors.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
BOX, G.E.P., and JONES, S. (1992): Designing products that are robust to the environment, Total Quality Management, 3, 265–282.
GRAYBILL, F.A. (1983): Matrices with Applications in Statistics. Belmont, California: Wadsworth, Inc.
LEON, R.V., SHOEMAKER, A.C. and KACKAR, R.N. (1987): Performance Measures Independent of Adjustment: An Explanation of Taguchi’s Signal to Noise Ratio, Technometrics, 29, 253–285.
MYERS, R.H., KHURI, A.I. and VINING, G. (1992): Response Surface Alternatives to the Taguchi Robust Parameter Design Approach, The American Statistician, 46, 131–139.
O’DONNELL, E.M. (1994): A Mean Squared Error of Prediction Approach to the Analysis of the Combined Array. Unpublished Ph. D. dissertation, University of Florida, Gainesville, Florida.
SEARLE, S.R. (1971): Linear Models. New York: Wiley.
SHOEMAKER, A.C., TSUI, K. and WU, C.F.J. (1991): Economical Experimentation Methods for Robust Design, Technometrics,33, 415–427.
STROUD, A.M. (1971): Approximate Calculation of Multiple Integrals. Englewood Cliffs, NJ: Prentice-Hall.
TAGUCHI, G., and WU, Y. (1985): Introduction to Off-line Quality Control. Nagoya Japan: Central Japan Quality Control Association.
WELCH, W.J., YU, T.K., KANG, S.M. and SACKS, J. (1990): Computer Experiments for Quality Control by Parameter Design, Journal of Quality Technology, 22, 15–22.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vining, G.G., O’Donnell, E.M. (1997). Prediction Properties of the Process Variance Using the Combined Array. In: Lenz, HJ., Wilrich, PT. (eds) Frontiers in Statistical Quality Control. Frontiers in Statistical Quality Control, vol 5. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-59239-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-59239-3_19
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-0984-8
Online ISBN: 978-3-642-59239-3
eBook Packages: Springer Book Archive