Authors: Myrta Rodríguez-Sifuentes; Douglas C. Montgomery; Connie M. Borror
Addresses: Instituto Tecnológico y de Estudios Superiores de Monterrey, Bulevar Enrique Mazón López No. 965, Hermosillo, Sonora, CP 83000, México. ' School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ 85287-8809, USA. ' Division of Mathematical and Natural Sciences, Arizona State University West, P.O. Box 37100, Phoenix, AZ 85069, USA
Abstract: Many experiments involve variables that can be easily controlled and variables that are difficult to control (noise). In robust design one goal is to determine the settings of the controllable factors that optimise the response while simultaneously minimise the variability transmitted to the response from the noise variables. This simultaneous optimisation can be addressed using a model for the mean response and a model for the transmitted variability to fit both the control and noise variables. Combined array designs are widely used in these robust parameter design problems. We extend previous work in this area for a larger number of factors. Specifically, we investigate the prediction variance performance of a variety of combined array designs for five to 20 variables and various combinations of control and noise variables. The design region for the control variables is spherical. Prediction variance results for a number of designs and some recommendations for their use are provided.
Keywords: combined arrays; design comparison; fraction of design space plots; robust design; array design; variability; control variables; noise variables.
International Journal of Experimental Design and Process Optimisation, 2012 Vol.3 No.1, pp.1 - 32
Published online: 27 Feb 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article