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| Robuster Zentraler Zusammengesetzter Entwurf× | Response-Oberflächenmethode (ROM)× | |
|---|---|---|
| Fachgebiet | Versuchsplanung | Versuchsplanung |
| Familie≠ | Process / pipeline | Hypothesis test |
| Entstehungsjahr≠ | 1951 (CCD); robust integration from 1980s–1990s | 1951 |
| Urheber≠ | George E. P. Box & K. B. Wilson (CCD foundation); robust extension via Taguchi and Myers–Montgomery tradition | George E. P. Box & K. B. Wilson |
| Typ≠ | Experimental design with robust optimization | Second-order polynomial response surface model |
| 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 | 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. link ↗ |
| Aliasnamen≠ | Robust CCD, CCD with robust optimization, robust RSM with CCD, robust response surface CCD | RSM, Central Composite Design, Box-Behnken Design, CCD |
| Verwandt≠ | 4 | 7 |
| 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. | Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics. |
| ScholarGateDatensatz ↗ |
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