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| Progettazione fattoriale frazionaria assistita da simulazione× | Metodologia delle Superfici di Risposta (RSM)× | |
|---|---|---|
| Campo | Disegno sperimentale | Disegno sperimentale |
| Famiglia≠ | Process / pipeline | Hypothesis test |
| Anno di origine≠ | FFD: 1950s; simulation integration: 1980s–2000s | 1951 |
| Ideatore≠ | Box, Hunter & Hunter (FFD basis); Kleijnen and others (simulation integration) | George E. P. Box & K. B. Wilson |
| Tipo≠ | Experimental design with computational augmentation | Second-order polynomial response surface model |
| Fonte seminale≠ | Kleijnen, J. P. C. (2008). Design and Analysis of Simulation Experiments. Springer. ISBN: 978-0387718125 | 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≠ | SA-FFD, virtual fractional factorial design, computer-aided fractional factorial design, simulation-based FFD | RSM, Central Composite Design, Box-Behnken Design, CCD |
| Correlati≠ | 4 | 7 |
| Sintesi≠ | Simulation-assisted fractional factorial design (SA-FFD) combines the statistical efficiency of fractional factorial experimentation with computerized simulation models to screen and estimate factor effects when physical experiments are too costly, hazardous, or time-consuming. A carefully chosen subset of factor-level combinations — the fractional factorial array — is executed inside a validated simulation model instead of (or alongside) a real process, dramatically reducing resource requirements while preserving the ability to identify main effects and low-order interactions. | 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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