Linganisha mbinu

	Jaribio la Adaptive Fractional Factorial	Ubunifu Mchanganyiko wa Kati
Nyanja	Muundo wa Majaribio	Muundo wa Majaribio
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1950s–1960s (classical FFD); adaptive extensions formalized in 1990s–2000s	1951
Mwanzilishi	Box, Hunter, and collaborators (adaptive/sequential extension of classical fractional factorial work)	George E. P. Box and K. B. Wilson
Aina	Experimental design strategy	Response surface experimental design
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	2	3
Muhtasari	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.