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| Риск-базиран дизайн на Бокс-Бенкен× | Планиране на експерименти× | |
|---|---|---|
| Област | Планиране на експеримента | Планиране на експеримента |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 2005–2009 (QbD-era integration of risk assessment with BBD) | 1935 |
| Създател≠ | Box & Behnken (BBD, 1960); risk integration formalized under ICH Q8/Q9 pharmaceutical QbD frameworks (~2005–2009) | Ronald A. Fisher |
| Тип≠ | Response surface experimental design with risk prioritization | Experimental planning framework |
| Основополагащ източник≠ | 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 ↗ | Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗ |
| Други названия | Risk-based BBD, Risk-prioritized Box-Behnken, QbD Box-Behnken design, Risk-informed RSM | DOE, experimental design, factorial experimentation, planned experimentation |
| Свързани≠ | 4 | 3 |
| Резюме≠ | Risk-based Box-Behnken Design combines the classical three-level Box-Behnken response surface design with a formal risk assessment step — typically a risk ranking tool such as FMEA or Ishikawa analysis — to prioritize which process or formulation factors deserve experimental investigation. Widely adopted in pharmaceutical Quality by Design (QbD) and engineering process optimization, the approach ensures that experimental resources are directed toward the factor combinations most likely to affect product quality or process performance, reducing unnecessary runs while preserving predictive power. | Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences. |
| ScholarGateНабор от данни ↗ |
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