Сравнение методов

	Робастный полный факторный эксперимент	Методология поверхности отклика (RSM)	Робастное дробное факторное проектирование
Область	Планирование эксперимента	Планирование эксперимента	Планирование эксперимента
Семейство	Process / pipeline	Hypothesis test	Process / pipeline
Год появления	1980s–1990s	1951	1980s (Taguchi's crossed-array approach); fractional factorial roots 1935–1945
Автор метода	Genichi Taguchi (robustness principles); formalized in combined-array form by Shoemaker, Tsui, and Wu (1991)	George E. P. Box & K. B. Wilson	Genichi Taguchi (robust parameter design); fractional factorial foundations by Ronald Fisher and Frank Yates
Тип	Experimental design with noise-factor control	Second-order polynomial response surface model	Experimental design / robust parameter design
Основополагающий источник	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.	Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443
Другие названия	robust 2^k design, full factorial robust parameter design, robust FFD, noise-factor full factorial	RSM, Central Composite Design, Box-Behnken Design, CCD	robust FFD, robust fractional factorial experiment, crossed-array fractional factorial, Taguchi-style fractional factorial
Связанные	2	7	2
Сводка	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.	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.