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| Методология на повърхността на отговора, подпомогната от оптимизация× | Дизайн на Бокс-Бенкен× | |
|---|---|---|
| Област | Планиране на експеримента | Планиране на експеримента |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1951 (RSM); 1980 (desirability-function optimization formalized) | 1960 |
| Създател≠ | Derringer & Suich (desirability function); Box & Wilson (RSM foundation) | George E. P. Box and Donald W. Behnken |
| Тип≠ | Hybrid experimental-optimization framework | Response surface design (incomplete three-level factorial) |
| Основополагащ източник≠ | Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗ | 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 ↗ |
| Други названия | OA-RSM, RSM with optimization, desirability-based RSM, multi-response RSM optimization | BBD, Box-Behnken, Box-Behnken RSM design, three-level incomplete factorial design |
| Свързани≠ | 5 | 3 |
| Резюме≠ | Optimization-assisted RSM couples a second-order response surface model with a mathematical optimization routine — most commonly Derringer and Suich's desirability function, but also genetic algorithms or gradient-based solvers — to locate the factor settings that simultaneously satisfy multiple quality or performance objectives. The result is a data-driven recommendation for optimal process or product conditions, supported by a polynomial model fitted to a structured experimental design. | The Box-Behnken design (BBD) is an efficient response surface methodology design that fits a full second-order polynomial model using three levels of each factor. Introduced by Box and Behnken in 1960, it places experimental points at the midpoints of the edges of a hypercube and at the center, avoiding the corner points where all factors are simultaneously at their extreme levels. This structure makes BBD particularly attractive when extreme-level combinations are physically impossible, costly, or unsafe to test. |
| ScholarGateНабор от данни ↗ |
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