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| Sensitivitätsanalyse mit Box-Behnken-Design× | Response-Oberflächenmethode (ROM)× | |
|---|---|---|
| Fachgebiet | Versuchsplanung | Versuchsplanung |
| Familie≠ | Process / pipeline | Hypothesis test |
| Entstehungsjahr≠ | 1960 (BBD); sensitivity integration formalized 2000s–2010s | 1951 |
| Urheber≠ | Box & Behnken (design, 1960); Saltelli et al. (sensitivity framework, 2000s) | George E. P. Box & K. B. Wilson |
| Typ≠ | Integrated experimental-design and sensitivity-analysis technique | Second-order polynomial response surface model |
| Wegweisende Quelle≠ | Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475. 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. link ↗ |
| Aliasnamen≠ | SA-BBD, Box-Behnken sensitivity analysis, BBD with sensitivity analysis, sensitivity-augmented Box-Behnken design | RSM, Central Composite Design, Box-Behnken Design, CCD |
| Verwandt≠ | 5 | 7 |
| Zusammenfassung≠ | Sensitivity analysis with Box-Behnken design combines a resource-efficient three-level response surface experiment with a systematic assessment of how much each input factor drives variation in the response. The Box-Behnken design (BBD) fits a second-order polynomial model using fewer runs than a full central composite design, while the overlaid sensitivity analysis quantifies each factor's relative influence — helping engineers and researchers distinguish the vital few drivers from the inconsequential many. | 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. |
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