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| Robust Full Factorial Design× | Robust Fractional Factorial Design× | |
|---|---|---|
| Област | Планиране на експеримента | Планиране на експеримента |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1980s–1990s | 1980s (Taguchi's crossed-array approach); fractional factorial roots 1935–1945 |
| Създател≠ | 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 |
| Тип≠ | Experimental design with noise-factor control | Experimental design / robust parameter design |
| Основополагащ източник≠ | 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 |
| Други названия | 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 |
| Свързани | 2 | 2 |
| Резюме≠ | 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. |
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