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| Adaptivt fraktionerat faktoriellt experiment× | Central Composite Design× | |
|---|---|---|
| Ämnesområde | Försöksplanering | Försöksplanering |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 1950s–1960s (classical FFD); adaptive extensions formalized in 1990s–2000s | 1951 |
| Upphovsperson≠ | Box, Hunter, and collaborators (adaptive/sequential extension of classical fractional factorial work) | George E. P. Box and K. B. Wilson |
| Typ≠ | Experimental design strategy | Response surface experimental design |
| Ursprungskälla≠ | Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley-Interscience. ISBN: 978-0471718130 | 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 ↗ |
| Alias | adaptive FFE, sequential fractional factorial design, adaptive screening design, adaptive factor screening | CCD, Box-Wilson design, central composite response surface design, rotatable central composite design |
| Närliggande≠ | 2 | 3 |
| Sammanfattning≠ | An adaptive fractional factorial experiment combines the resource-efficiency of fractional factorial designs with a sequential, data-driven strategy for selecting which factors and interactions to investigate next. Rather than committing all experimental runs upfront, the researcher analyses results from an initial fraction and uses those findings to guide subsequent rounds of experimentation — augmenting, folding, or redirecting the design until the active factors and optimal settings are identified with sufficient precision. | 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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