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| Robuste Response-Surface-Methodologie× | Zentrales Komposit-Design× | |
|---|---|---|
| Fachgebiet | Versuchsplanung | Versuchsplanung |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1990 | 1951 |
| Urheber≠ | G. G. Vining and Raymond H. Myers (dual response formulation) | George E. P. Box and K. B. Wilson |
| Typ≠ | Experimental optimization technique | Response surface experimental design |
| Wegweisende Quelle≠ | 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. DOI ↗ | 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 ↗ |
| Aliasnamen | Robust RSM, dual response surface methodology, robust parameter design via RSM, mean-variance RSM | CCD, Box-Wilson design, central composite response surface design, rotatable central composite design |
| Verwandt | 3 | 3 |
| Zusammenfassung≠ | 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. | 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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