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| Optymalizacja wspomagana ułamkowym planem czynnikowym× | Box-Behnken Design× | |
|---|---|---|
| Dziedzina | Planowanie eksperymentów | Planowanie eksperymentów |
| Rodzina | Process / pipeline | Process / pipeline |
| Rok powstania≠ | 1960s–1980s (D-optimality: Kiefer & Wolfowitz 1959; coordinate-exchange: Meyer & Nachtsheim 1995) | 1960 |
| Twórca≠ | A. C. Atkinson, A. N. Donev (optimality criteria); V. V. Federov (exchange algorithms) | George E. P. Box and Donald W. Behnken |
| Typ≠ | Optimal experimental design / computer-generated DOE | Response surface design (incomplete three-level factorial) |
| Źródło pierwotne≠ | 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., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475. DOI ↗ |
| Inne nazwy | optimal fractional factorial design, algorithmically optimized FFD, computer-aided fractional factorial design, D-optimal fractional factorial design | BBD, Box-Behnken, Box-Behnken RSM design, three-level incomplete factorial design |
| Pokrewne≠ | 4 | 3 |
| Podsumowanie≠ | 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. | The Box-Behnken design (BBD) is an efficient response surface methodology design that fits a full second-order polynomial model using three levels of each factor. Introduced by Box and Behnken in 1960, it places experimental points at the midpoints of the edges of a hypercube and at the center, avoiding the corner points where all factors are simultaneously at their extreme levels. This structure makes BBD particularly attractive when extreme-level combinations are physically impossible, costly, or unsafe to test. |
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