Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Plan factoriel complet appliqué aux contextes industriels× | Méthodologie des surfaces de réponse (RSM)× | |
|---|---|---|
| Domaine | Plans d'expériences | Plans d'expériences |
| Famille≠ | Process / pipeline | Hypothesis test |
| Année d'origine≠ | 1926 (foundational); industrially systematized by Box, Hunter & Hunter ~1950s–1978 | 1951 |
| Auteur d'origine≠ | Ronald A. Fisher | George E. P. Box & K. B. Wilson |
| Type≠ | Experimental design / factorial experiment | Second-order polynomial response surface model |
| Source fondatrice≠ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443 | 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 ↗ |
| Alias≠ | industrial FFD, full factorial experiment, complete factorial design, 2^k factorial design | RSM, Central Composite Design, Box-Behnken Design, CCD |
| Apparentées≠ | 3 | 7 |
| Résumé≠ | Full factorial design (FFD) applied in industrial settings is a structured experimental methodology in which every combination of factor levels is tested, enabling engineers to quantify main effects and all interaction effects among process or product variables. Widely used in manufacturing, chemical processing, materials science, and quality engineering, it provides a complete picture of how input factors jointly influence a response variable such as yield, strength, or defect rate. | 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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