方法对比
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| 优化辅助的中心复合设计× | 响应面方法 (RSM)× | |
|---|---|---|
| 领域 | 实验设计 | 实验设计 |
| 方法族≠ | Process / pipeline | Hypothesis test |
| 起源年份≠ | 1951 (CCD); optimization coupling formalized 1970s–1990s | 1951 |
| 提出者≠ | Box & Wilson (CCD, 1951); optimization integration by Myers, Montgomery & colleagues | George E. P. Box & K. B. Wilson |
| 类型≠ | Experimental design with mathematical optimization | Second-order polynomial response surface model |
| 开创性文献≠ | 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 | 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 ↗ |
| 别名≠ | CCD with optimization, optimized CCD, RSM-CCD optimization, central composite design with response optimization | RSM, Central Composite Design, Box-Behnken Design, CCD |
| 相关≠ | 3 | 7 |
| 摘要≠ | 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. | 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. |
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