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| Hybrides Versuchsdesign× | Response-Oberflächenmethode (ROM)× | |
|---|---|---|
| Fachgebiet | Versuchsplanung | Versuchsplanung |
| Familie≠ | Process / pipeline | Hypothesis test |
| Entstehungsjahr≠ | 1989–2000s | 1951 |
| Urheber≠ | Multiple contributors; notably Sacks, Welch, Mitchell & Wynn (computer experiments); broader hybrid concept developed across 1980s–2000s | George E. P. Box & K. B. Wilson |
| Typ≠ | Combined experimental design strategy | Second-order polynomial response surface model |
| Wegweisende Quelle≠ | Santner, T. J., Williams, B. J., & Notz, W. I. (2003). The Design and Analysis of Computer Experiments. Springer. ISBN: 978-1441929921 | 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≠ | hybrid DOE, combined experimental design, mixed experimental design, hybrid experimental strategy | RSM, Central Composite Design, Box-Behnken Design, CCD |
| Verwandt≠ | 4 | 7 |
| Zusammenfassung≠ | Hybrid design of experiments (hybrid DOE) combines two or more experimental design strategies within a single study to exploit the complementary strengths of each. Common combinations include factorial or fractional-factorial arrays paired with computer simulation runs, space-filling Latin hypercube designs merged with response surface augmentations, or Taguchi orthogonal arrays integrated with response surface methodology. The approach is widely used when a single design type cannot efficiently cover all phases of an engineering investigation — from screening through to optimization. | 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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