Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Diseño Compuesto Central Robusto× | Diseño Compuesto Central× | |
|---|---|---|
| Campo | Diseño experimental | Diseño experimental |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 1951 (CCD); robust integration from 1980s–1990s | 1951 |
| Autor original≠ | George E. P. Box & K. B. Wilson (CCD foundation); robust extension via Taguchi and Myers–Montgomery tradition | George E. P. Box and K. B. Wilson |
| Tipo≠ | Experimental design with robust optimization | Response surface experimental design |
| Fuente seminal≠ | Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2009). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (3rd ed.). Wiley. ISBN: 978-0470174463 | 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 | Robust CCD, CCD with robust optimization, robust RSM with CCD, robust response surface CCD | CCD, Box-Wilson design, central composite response surface design, rotatable central composite design |
| Relacionados≠ | 4 | 3 |
| Resumen≠ | Robust Central Composite Design (Robust CCD) combines the efficient quadratic fitting capability of the central composite design with robust optimization principles to find factor settings that simultaneously achieve a target mean response and minimize the effect of uncontrollable noise factors on response variability. It is widely applied in manufacturing, chemical engineering, and product development when both performance and consistency under real-world variation are critical. | 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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