方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 优化辅助 Box-Behnken 设计× | 优化辅助的中心复合设计× | |
|---|---|---|
| 领域 | 实验设计 | 实验设计 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1960 (BBD); optimization integration established 1980s–1990s | 1951 (CCD); optimization coupling formalized 1970s–1990s |
| 提出者≠ | Box & Behnken (design); Derringer & Suich (desirability optimization) | Box & Wilson (CCD, 1951); optimization integration by Myers, Montgomery & colleagues |
| 类型≠ | Experimental design with post-modeling optimization | Experimental design with mathematical optimization |
| 开创性文献≠ | 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 ↗ | Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2009). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (3rd ed.). Wiley. ISBN: 978-0470174463 |
| 别名 | BBD with optimization, Box-Behnken design optimization, RSM-BBD optimization, Box-Behnken response optimization | CCD with optimization, optimized CCD, RSM-CCD optimization, central composite design with response optimization |
| 相关≠ | 5 | 3 |
| 摘要≠ | Optimization-assisted Box-Behnken design (BBD) combines the Box-Behnken three-level experimental design with a formal optimization step to locate factor settings that maximize, minimize, or hit a target for one or more responses. BBD fits a second-order response surface model using fewer runs than a full factorial, and the optimization stage — typically via desirability functions or numerical search — then exploits that fitted model to identify the true optimum within the experimental region. | Optimization-assisted central composite design (CCD) combines the rotatable, second-order experimental layout of central composite design with mathematical optimization algorithms — typically desirability functions, response surface optimization, or metaheuristics — to find the factor settings that simultaneously maximize, minimize, or hit target values for one or more response variables. It is the most widely applied response-surface optimization workflow in chemical, pharmaceutical, food science, and manufacturing engineering. |
| ScholarGate数据集 ↗ |
|
|