Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Diseño factorial completo para aplicaciones industriales× | Diseño Compuesto Central× | |
|---|---|---|
| Campo | Diseño experimental | Diseño experimental |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1926 (foundational); industrially systematized by Box, Hunter & Hunter ~1950s–1978 | 1951 |
| Autor original≠ | Ronald A. Fisher | George E. P. Box and K. B. Wilson |
| Tipo≠ | Experimental design / factorial experiment | Response surface experimental design |
| Fuente seminal≠ | 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 |
| Relacionados | 3 | 3 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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