Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Conception d'expériences assistée par simulation× | Planification Composite Centrale× | |
|---|---|---|
| Domaine | Plans d'expériences | Plans d'expériences |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1970s–1990s (formalized with computer experimentation growth) | 1951 |
| Auteur d'origine≠ | Multiple contributors; systematized by Jack P.C. Kleijnen and Thomas J. Santner et al. | George E. P. Box and K. B. Wilson |
| Type≠ | Hybrid experimental-computational method | Response surface experimental design |
| Source fondatrice≠ | Santner, T. J., Williams, B. J., & Notz, W. I. (2003). The Design and Analysis of Computer Experiments. Springer. ISBN: 978-0387954202 | 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 | Simulation-based DoE, Virtual DoE, Computer-aided DoE, SA-DoE | CCD, Box-Wilson design, central composite response surface design, rotatable central composite design |
| Apparentées≠ | 5 | 3 |
| Résumé≠ | Simulation-assisted design of experiments (SA-DoE) integrates computational simulation tools — such as finite element analysis (FEA), computational fluid dynamics (CFD), or discrete-event simulation — with classical DoE principles to systematically explore the factor space of a system. Rather than running costly or hazardous physical trials, researchers execute a structured set of virtual experiments across selected factor combinations, then fit a surrogate model to the simulation outputs to understand main effects, interactions, and optimal settings. | 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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