Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Многофакторный дробный факторный эксперимент× | Планирование эксперимента× | |
|---|---|---|
| Область | Планирование эксперимента | Планирование эксперимента |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1961 (fractional factorial foundation); 1980 (multi-response desirability approach) | 1935 |
| Автор метода≠ | George E.P. Box, J. Stuart Hunter, and William G. Hunter (fractional factorial basis); Derringer & Suich (multi-response desirability extension) | Ronald A. Fisher |
| Тип≠ | Experimental design with simultaneous multi-response optimization | Experimental planning framework |
| Основополагающий источник≠ | Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗ | Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗ |
| Другие названия | MRFFD, multi-response FFD, multi-objective fractional factorial design, simultaneous multi-response fractional factorial | DOE, experimental design, factorial experimentation, planned experimentation |
| Связанные≠ | 4 | 3 |
| Сводка≠ | Multi-response fractional factorial design (MRFFD) applies a resolution-efficient fractional factorial experiment to study multiple response variables simultaneously. By running only a carefully chosen fraction of the full factorial treatment combinations, the experimenter gathers enough information to fit individual response models for each output and then optimize all responses jointly — typically via a composite desirability function — while keeping the number of experimental runs tractable. | Design of Experiments (DOE) is a systematic framework for planning, conducting, and analyzing controlled experiments to determine how multiple input factors simultaneously affect one or more responses. Introduced by Ronald A. Fisher in 1935, DOE allows researchers and engineers to identify causal relationships, quantify factor effects, and find optimal settings efficiently — using far fewer runs than one-factor-at-a-time approaches. It is foundational in engineering, manufacturing, agriculture, and applied sciences. |
| ScholarGateНабор данных ↗ |
|
|