Compara mètodes

	Disseny factorial complet robust	Disseny factorial fraccionari robust
Camp	Disseny experimental	Disseny experimental
Família	Process / pipeline	Process / pipeline
Any d'origen	1980s–1990s	1980s (Taguchi's crossed-array approach); fractional factorial roots 1935–1945
Autor original	Genichi Taguchi (robustness principles); formalized in combined-array form by Shoemaker, Tsui, and Wu (1991)	Genichi Taguchi (robust parameter design); fractional factorial foundations by Ronald Fisher and Frank Yates
Tipus	Experimental design with noise-factor control	Experimental design / robust parameter design
Font seminal	Phadke, M. S. (1989). Quality Engineering Using Robust Design. Prentice Hall. ISBN: 978-0137451678	Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443
Àlies	robust 2^k design, full factorial robust parameter design, robust FFD, noise-factor full factorial	robust FFD, robust fractional factorial experiment, crossed-array fractional factorial, Taguchi-style fractional factorial
Relacionats	2	2
Resum	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.	Robust fractional factorial design combines the run-count efficiency of fractional factorial arrays with Taguchi's robust parameter design philosophy. By simultaneously manipulating control factors (inner array) and noise factors (outer array) — each structured as a fractional factorial — the method identifies factor settings that minimize product or process variation due to uncontrollable conditions, without requiring a full factorial experiment.