Módszerek összehasonlítása

	Kockázatalapú teljes faktorális tervezés	Robuszt Teljes Faktoros Terv
Tudományterület	Kísérlettervezés	Kísérlettervezés
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	2000s (formal integration with risk frameworks circa 2005–2009)	1980s–1990s
Megalkotó	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)
Típus	Structured experimental design with risk-informed factor prioritization	Experimental design with noise-factor control
Alapmű	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
Alternatív nevek	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
Kapcsolódó	3	2
Összefoglaló	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.