手法を比較

	ロバストフラクショナルファクトリアルデザイン	中心複合計画法	応答曲面法 (RSM)
分野	実験計画法	実験計画法	実験計画法
系統	Process / pipeline	Process / pipeline	Hypothesis test
提唱年	1980s (Taguchi's crossed-array approach); fractional factorial roots 1935–1945	1951	1951
提唱者	Genichi Taguchi (robust parameter design); fractional factorial foundations by Ronald Fisher and Frank Yates	George E. P. Box and K. B. Wilson	George E. P. Box & K. B. Wilson
種類	Experimental design / robust parameter design	Response surface experimental design	Second-order polynomial response surface model
原典	Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443	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.
別名	robust FFD, robust fractional factorial experiment, crossed-array fractional factorial, Taguchi-style fractional factorial	CCD, Box-Wilson design, central composite response surface design, rotatable central composite design	RSM, Central Composite Design, Box-Behnken Design, CCD
関連	2	3	7
概要	Robust fractional factorial design combines the run-count efficiency of fractional factorial arrays with Taguchi's robust parameter design philosophy. By simultaneously manipulating control factors (inner array) and noise factors (outer array) — each structured as a fractional factorial — the method identifies factor settings that minimize product or process variation due to uncontrollable conditions, without requiring a full factorial experiment.	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.