Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Analýza citlivosti s centrálním kompozitním designem× | Centrální kompozitní design× | |
|---|---|---|
| Obor | Plánování experimentů | Plánování experimentů |
| Rodina | Process / pipeline | Process / pipeline |
| Rok vzniku≠ | 1951 (CCD); SA integration throughout 1970s–2000s | 1951 |
| Tvůrce≠ | G. E. P. Box and K. B. Wilson (CCD); sensitivity analysis formalised within RSM by Montgomery and subsequent practitioners | George E. P. Box and K. B. Wilson |
| Typ≠ | Quantitative experimental design with post-hoc sensitivity assessment | Response surface experimental design |
| Původní zdroj | 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. link ↗ | 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. DOI ↗ |
| Další názvy | SA-CCD, CCD sensitivity analysis, RSM sensitivity analysis, response surface sensitivity study | CCD, Box-Wilson design, central composite response surface design, rotatable central composite design |
| Příbuzné≠ | 4 | 3 |
| Shrnutí≠ | Sensitivity analysis with Central Composite Design (CCD) combines a structured, space-filling experimental layout with a systematic examination of how much each input factor drives changes in the response. CCD supports estimation of a full quadratic response surface model; sensitivity analysis then interrogates that model to rank factors by influence, identify interactions, and map the performance landscape — guiding engineers and researchers toward robust operating conditions and efficient optimisation. | 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. |
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