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| Optymalizacja wspomagana centralnym projektem kompozytowym× | Central Composite Design× | |
|---|---|---|
| Dziedzina | Planowanie eksperymentów | Planowanie eksperymentów |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1951 (CCD); optimization coupling formalized 1970s–1990s | 1951 |
| Twórca≠ | Box & Wilson (CCD, 1951); optimization integration by Myers, Montgomery & colleagues | George E. P. Box and K. B. Wilson |
| Typ≠ | Experimental design with mathematical optimization | Response surface experimental design |
| Źródło pierwotne≠ | Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2009). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (3rd ed.). Wiley. ISBN: 978-0470174463 | 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 ↗ |
| Inne nazwy | CCD with optimization, optimized CCD, RSM-CCD optimization, central composite design with response optimization | CCD, Box-Wilson design, central composite response surface design, rotatable central composite design |
| Pokrewne | 3 | 3 |
| Podsumowanie≠ | Optimization-assisted central composite design (CCD) combines the rotatable, second-order experimental layout of central composite design with mathematical optimization algorithms — typically desirability functions, response surface optimization, or metaheuristics — to find the factor settings that simultaneously maximize, minimize, or hit target values for one or more response variables. It is the most widely applied response-surface optimization workflow in chemical, pharmaceutical, food science, and manufacturing engineering. | 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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