Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Delineamento Fatorial Fracionado Assistido por Simulação× | Delineamento Composto Central× | |
|---|---|---|
| Área | Delineamento experimental | Delineamento experimental |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | FFD: 1950s; simulation integration: 1980s–2000s | 1951 |
| Autor original≠ | Box, Hunter & Hunter (FFD basis); Kleijnen and others (simulation integration) | George E. P. Box and K. B. Wilson |
| Tipo≠ | Experimental design with computational augmentation | Response surface experimental design |
| Fonte seminal≠ | Kleijnen, J. P. C. (2008). Design and Analysis of Simulation Experiments. Springer. ISBN: 978-0387718125 | 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 ↗ |
| Outros nomes | SA-FFD, virtual fractional factorial design, computer-aided fractional factorial design, simulation-based FFD | CCD, Box-Wilson design, central composite response surface design, rotatable central composite design |
| Relacionados≠ | 4 | 3 |
| Resumo≠ | Simulation-assisted fractional factorial design (SA-FFD) combines the statistical efficiency of fractional factorial experimentation with computerized simulation models to screen and estimate factor effects when physical experiments are too costly, hazardous, or time-consuming. A carefully chosen subset of factor-level combinations — the fractional factorial array — is executed inside a validated simulation model instead of (or alongside) a real process, dramatically reducing resource requirements while preserving the ability to identify main effects and low-order interactions. | 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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