Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Design Fatorial Fracionado Assistido por Otimização× | Metodologia de Superfície de Resposta (RSM)× | |
|---|---|---|
| Área | Delineamento experimental | Delineamento experimental |
| Família≠ | Process / pipeline | Hypothesis test |
| Ano de origem≠ | 1960s–1980s (D-optimality: Kiefer & Wolfowitz 1959; coordinate-exchange: Meyer & Nachtsheim 1995) | 1951 |
| Autor original≠ | A. C. Atkinson, A. N. Donev (optimality criteria); V. V. Federov (exchange algorithms) | George E. P. Box & K. B. Wilson |
| Tipo≠ | Optimal experimental design / computer-generated DOE | Second-order polynomial response surface model |
| Fonte seminal≠ | Atkinson, A. C., Donev, A. N., & Tobias, R. D. (2007). Optimum Experimental Designs, with SAS. Oxford University Press. ISBN: 978-0199296606 | 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 ↗ |
| Outros nomes≠ | optimal fractional factorial design, algorithmically optimized FFD, computer-aided fractional factorial design, D-optimal fractional factorial design | RSM, Central Composite Design, Box-Behnken Design, CCD |
| Relacionados≠ | 4 | 7 |
| Resumo≠ | Optimization-assisted fractional factorial design (OA-FFD) combines classical fractional factorial screening with algorithmic optimality criteria — such as D-, I-, or A-optimality — to construct experiment matrices that maximize statistical efficiency. Instead of relying solely on standard orthogonal-array tables, a computer algorithm selects the best subset of runs from a candidate set, enabling experimenters to handle irregular factor constraints, mixed factor types, and custom run sizes that standard tables cannot accommodate. | Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics. |
| ScholarGateConjunto de dados ↗ |
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