Comparer des méthodes

	Plan d'expériences composite central basé sur le risque	Méthodologie des surfaces de réponse (RSM)
Domaine	Plans d'expériences	Plans d'expériences
Famille	Process / pipeline	Hypothesis test
Année d'origine	1951 (CCD); risk-based integration emerged in applied engineering literature from the 1990s onward	1951
Auteur d'origine	Foundational CCD: George E. P. Box & K. B. Wilson (1951); risk integration adapted from engineering risk analysis traditions	George E. P. Box & K. B. Wilson
Type	Experimental design with integrated risk assessment	Second-order polynomial response surface model
Source fondatrice	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.	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.
Alias	Risk-informed CCD, CCD with risk assessment, Uncertainty-aware central composite design, Risk-integrated RSM	RSM, Central Composite Design, Box-Behnken Design, CCD
Apparentées	5	7
Résumé	Risk-based Central Composite Design (Risk-based CCD) integrates formal risk identification and uncertainty quantification into the classical CCD framework. By coupling the rotatable second-order experimental structure of CCD with probabilistic risk metrics, engineers and scientists can simultaneously optimize process responses and characterize the risk of unacceptable outcomes — making it particularly valuable in regulated industries such as pharmaceuticals, chemical engineering, and advanced manufacturing.	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.