方法对比
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| 多响应全因子设计× | 实验设计× | |
|---|---|---|
| 领域 | 实验设计 | 实验设计 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1950s–1980s | 1935 |
| 提出者≠ | Douglas C. Montgomery (factorial framework); Derringer & Suich (multi-response desirability optimization) | Ronald A. Fisher |
| 类型≠ | Experimental design with multi-objective optimization | Experimental planning framework |
| 开创性文献≠ | Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443 | Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗ |
| 别名 | MRFFD, multi-response FFD, multiple-response full factorial, multi-objective full factorial design | DOE, experimental design, factorial experimentation, planned experimentation |
| 相关 | 3 | 3 |
| 摘要≠ | Multi-response full factorial design extends the classic full factorial experiment by measuring and jointly optimizing two or more response variables at the same time. Every combination of all factor levels is tested, providing complete main-effect and interaction information for each response. A desirability function or Pareto-front approach then reconciles competing responses into a single optimal factor setting, making this the method of choice when engineering or process goals involve trade-offs among several quality characteristics simultaneously. | 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. |
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