विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| Robust Fractional Factorial Design× | सेंट्रल कंपोजिट डिज़ाइन× | |
|---|---|---|
| क्षेत्र | प्रयोगात्मक अभिकल्प | प्रयोगात्मक अभिकल्प |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 1980s (Taguchi's crossed-array approach); fractional factorial roots 1935–1945 | 1951 |
| प्रवर्तक≠ | Genichi Taguchi (robust parameter design); fractional factorial foundations by Ronald Fisher and Frank Yates | George E. P. Box and K. B. Wilson |
| प्रकार≠ | Experimental design / robust parameter design | Response surface experimental design |
| मौलिक स्रोत≠ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443 | Box, G. E. P., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society: Series B, 13(1), 1–45. DOI ↗ |
| उपनाम | robust FFD, robust fractional factorial experiment, crossed-array fractional factorial, Taguchi-style fractional factorial | CCD, Box-Wilson design, central composite response surface design, rotatable central composite design |
| संबंधित≠ | 2 | 3 |
| सारांश≠ | 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. | Central Composite Design (CCD) is a second-order response surface design that allows researchers to efficiently fit a full quadratic model relating multiple continuous input factors to one or more response variables. Introduced by Box and Wilson in 1951, it combines a factorial (or fractional factorial) core, axial (star) points, and center-point replicates into a single unified design, making it the most widely used design for process optimization in engineering, chemistry, and manufacturing. |
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