Methoden vergelijken

	Robuust Volledig Factorieel Ontwerp	Response Surface Methodology (RSM)
Vakgebied	Experimenteel ontwerp	Experimenteel ontwerp
Familie	Process / pipeline	Hypothesis test
Jaar van ontstaan	1980s–1990s	1951
Grondlegger	Genichi Taguchi (robustness principles); formalized in combined-array form by Shoemaker, Tsui, and Wu (1991)	George E. P. Box & K. B. Wilson
Type	Experimental design with noise-factor control	Second-order polynomial response surface model
Oorspronkelijke bron	Phadke, M. S. (1989). Quality Engineering Using Robust Design. Prentice Hall. ISBN: 978-0137451678	Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.
Aliassen	robust 2^k design, full factorial robust parameter design, robust FFD, noise-factor full factorial	RSM, Central Composite Design, Box-Behnken Design, CCD
Verwant	2	7
Samenvatting	Robust full factorial design extends the classical full factorial experiment by explicitly including noise factors — uncontrollable variables that cause performance variation in real-world conditions. By crossing all control factor levels with all noise factor levels in a single combined array, engineers identify control factor settings that maximize mean performance while minimizing sensitivity to noise, yielding products and processes that perform consistently across operating environments.	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.