方法对比
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| 优化辅助分数析因设计× | 中心复合设计× | |
|---|---|---|
| 领域 | 实验设计 | 实验设计 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1960s–1980s (D-optimality: Kiefer & Wolfowitz 1959; coordinate-exchange: Meyer & Nachtsheim 1995) | 1951 |
| 提出者≠ | A. C. Atkinson, A. N. Donev (optimality criteria); V. V. Federov (exchange algorithms) | George E. P. Box and K. B. Wilson |
| 类型≠ | Optimal experimental design / computer-generated DOE | Response surface experimental design |
| 开创性文献≠ | 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. DOI ↗ |
| 别名 | optimal fractional factorial design, algorithmically optimized FFD, computer-aided fractional factorial design, D-optimal fractional factorial design | CCD, Box-Wilson design, central composite response surface design, rotatable central composite design |
| 相关≠ | 4 | 3 |
| 摘要≠ | 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. | 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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