Сравнение методов

	Промышленное применение методологии поверхности отклика	Центральное композиционное планирование
Область	Планирование эксперимента	Планирование эксперимента
Семейство	Process / pipeline	Process / pipeline
Год появления	1951 (origin); widespread industrial adoption from 1980s onward	1951
Автор метода	George E. P. Box & K. B. Wilson; industrialized by Douglas Montgomery and colleagues	George E. P. Box and K. B. Wilson
Тип	Empirical optimization technique	Response surface experimental design
Основополагающий источник	Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley. ISBN: 978-1118916018	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.
Другие названия	Industrial RSM, RSM for manufacturing, process optimization RSM, industrial response surface analysis	CCD, Box-Wilson design, central composite response surface design, rotatable central composite design
Связанные	5	3
Сводка	Industrial Applications Response Surface Methodology (RSM) applies the classical Box-Wilson response surface framework to manufacturing and process engineering problems. It builds an empirical polynomial model linking controllable process inputs — such as temperature, pressure, feed rate, or catalyst concentration — to one or more quality responses, then mathematically locates the input settings that optimize those responses. It is the de-facto standard statistical tool for process characterization and optimization in chemical, mechanical, food, materials, and pharmaceutical manufacturing.	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.