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| 반응 표면 분석법 (RSM)× | 강건 부분 요인 설계× | |
|---|---|---|
| 분야 | 실험설계 | 실험설계 |
| 계열≠ | Hypothesis test | Process / pipeline |
| 기원 연도≠ | 1951 | 1980s (Taguchi's crossed-array approach); fractional factorial roots 1935–1945 |
| 창시자≠ | George E. P. Box & K. B. Wilson | Genichi Taguchi (robust parameter design); fractional factorial foundations by Ronald Fisher and Frank Yates |
| 유형≠ | Second-order polynomial response surface model | Experimental design / robust parameter design |
| 원전≠ | 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. link ↗ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443 |
| 별칭≠ | RSM, Central Composite Design, Box-Behnken Design, CCD | robust FFD, robust fractional factorial experiment, crossed-array fractional factorial, Taguchi-style fractional factorial |
| 관련≠ | 7 | 2 |
| 요약≠ | Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics. | 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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