Confronta i metodi

	Progetto Composito Centrale Robusto	Metodologia della Superficie di Risposta Robusta
Campo	Disegno sperimentale	Disegno sperimentale
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	1951 (CCD); robust integration from 1980s–1990s	1990
Ideatore	George E. P. Box & K. B. Wilson (CCD foundation); robust extension via Taguchi and Myers–Montgomery tradition	G. G. Vining and Raymond H. Myers (dual response formulation)
Tipo	Experimental design with robust optimization	Experimental optimization technique
Fonte seminale	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	Vining, G. G., & Myers, R. H. (1990). Combining Taguchi and response surface philosophies: A dual response approach. Journal of Quality Technology, 22(1), 38–45.
Alias	Robust CCD, CCD with robust optimization, robust RSM with CCD, robust response surface CCD	Robust RSM, dual response surface methodology, robust parameter design via RSM, mean-variance RSM
Correlati	4	3
Sintesi	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.	Robust Response Surface Methodology (Robust RSM) is an experimental optimization strategy that simultaneously fits two regression models — one for the mean response and one for its variance (or standard deviation) — across a designed experiment. By jointly optimizing these dual surfaces, engineers identify factor settings that hit a performance target while minimizing process variability, combining the empirical model-building power of classical RSM with the variance-reduction goals of robust parameter design.