Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Proiectare factorială completă bazată pe risc× | Proiectare robustă factorială completă× | |
|---|---|---|
| Domeniu | Design experimental | Design experimental |
| Familie | Process / pipeline | Process / pipeline |
| Anul apariției≠ | 2000s (formal integration with risk frameworks circa 2005–2009) | 1980s–1990s |
| Autorul original≠ | Developed at the intersection of classical factorial experimentation (Fisher, 1935) and formal risk analysis frameworks (ICH Q8/Q9, 2005–2009) | Genichi Taguchi (robustness principles); formalized in combined-array form by Shoemaker, Tsui, and Wu (1991) |
| Tip≠ | Structured experimental design with risk-informed factor prioritization | Experimental design with noise-factor control |
| Sursa seminală≠ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119113478 | Phadke, M. S. (1989). Quality Engineering Using Robust Design. Prentice Hall. ISBN: 978-0137451678 |
| Denumiri alternative | risk-informed full factorial design, RB-FFD, risk-prioritized factorial experiment, risk-based FFD | robust 2^k design, full factorial robust parameter design, robust FFD, noise-factor full factorial |
| Înrudite≠ | 3 | 2 |
| Rezumat≠ | Risk-based full factorial design integrates formal risk analysis — typically Failure Mode and Effects Analysis (FMEA) or a comparable risk-ranking tool — with a full factorial experiment to ensure that factors posing the greatest quality or safety risk receive exhaustive experimental coverage. All combinations of selected factor levels are run, but the selection of which factors to include and the range of their levels is explicitly guided by prior risk scores rather than purely by engineering intuition or resource availability. | Robust full factorial design extends the classical full factorial experiment by explicitly including noise factors — uncontrollable variables that cause performance variation in real-world conditions. By crossing all control factor levels with all noise factor levels in a single combined array, engineers identify control factor settings that maximize mean performance while minimizing sensitivity to noise, yielding products and processes that perform consistently across operating environments. |
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