Comparar métodos

	Diseño Experimental Fraccional Factorial Adaptativo	Diseño Compuesto Central	Metodología de Superficie de Respuesta (RSM)
Campo	Diseño experimental	Diseño experimental	Diseño experimental
Familia	Process / pipeline	Process / pipeline	Hypothesis test
Año de origen	1950s–1960s (classical FFD); adaptive extensions formalized in 1990s–2000s	1951	1951
Autor original	Box, Hunter, and collaborators (adaptive/sequential extension of classical fractional factorial work)	George E. P. Box and K. B. Wilson	George E. P. Box & K. B. Wilson
Tipo	Experimental design strategy	Response surface experimental design	Second-order polynomial response surface model
Fuente seminal	Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience. ISBN: 978-0471718130	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	adaptive FFE, sequential fractional factorial design, adaptive screening design, adaptive factor screening	CCD, Box-Wilson design, central composite response surface design, rotatable central composite design	RSM, Central Composite Design, Box-Behnken Design, CCD
Relacionados	2	3	7
Resumen	An adaptive fractional factorial experiment combines the resource-efficiency of fractional factorial designs with a sequential, data-driven strategy for selecting which factors and interactions to investigate next. Rather than committing all experimental runs upfront, the researcher analyses results from an initial fraction and uses those findings to guide subsequent rounds of experimentation — augmenting, folding, or redirecting the design until the active factors and optimal settings are identified with sufficient precision.	Central Composite Design (CCD) is a second-order response surface design that allows researchers to efficiently fit a full quadratic model relating multiple continuous input factors to one or more response variables. Introduced by Box and Wilson in 1951, it combines a factorial (or fractional factorial) core, axial (star) points, and center-point replicates into a single unified design, making it the most widely used design for process optimization in engineering, chemistry, and 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.