Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Conception de Box-Behnken assistée par optimisation× | Planification Composite Centrale× | |
|---|---|---|
| Domaine | Plans d'expériences | Plans d'expériences |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1960 (BBD); optimization integration established 1980s–1990s | 1951 |
| Auteur d'origine≠ | Box & Behnken (design); Derringer & Suich (desirability optimization) | George E. P. Box and K. B. Wilson |
| Type≠ | Experimental design with post-modeling optimization | Response surface experimental design |
| Source fondatrice≠ | 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 ↗ | 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 ↗ |
| Alias | BBD with optimization, Box-Behnken design optimization, RSM-BBD optimization, Box-Behnken response optimization | CCD, Box-Wilson design, central composite response surface design, rotatable central composite design |
| Apparentées≠ | 5 | 3 |
| Résumé≠ | 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. | 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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