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| Robuster Zentraler Zusammengesetzter Entwurf× | Robuste Response-Surface-Methodologie× | |
|---|---|---|
| Fachgebiet | Versuchsplanung | Versuchsplanung |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1951 (CCD); robust integration from 1980s–1990s | 1990 |
| Urheber≠ | 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) |
| Typ≠ | Experimental design with robust optimization | Experimental optimization technique |
| Wegweisende Quelle≠ | 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. DOI ↗ |
| Aliasnamen | 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 |
| Verwandt≠ | 4 | 3 |
| Zusammenfassung≠ | 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. |
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