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Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Progettazione Full Factorial per Applicazioni Industriali× | Disegno Centrale Composito× | |
|---|---|---|
| Campo | Disegno sperimentale | Disegno sperimentale |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1926 (foundational); industrially systematized by Box, Hunter & Hunter ~1950s–1978 | 1951 |
| Ideatore≠ | Ronald A. Fisher | George E. P. Box and K. B. Wilson |
| Tipo≠ | Experimental design / factorial experiment | Response surface experimental design |
| Fonte seminale≠ | 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. DOI ↗ |
| Alias | industrial FFD, full factorial experiment, complete factorial design, 2^k factorial design | CCD, Box-Wilson design, central composite response surface design, rotatable central composite design |
| Correlati | 3 | 3 |
| Sintesi≠ | 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. | 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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