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| Методология на повърхността на отговора, базирана на риска× | Методология на робастната повърхност на отклика× | |
|---|---|---|
| Област | Планиране на експеримента | Планиране на експеримента |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1990s–2000s (risk-based extensions) | 1990 |
| Създател≠ | Builds on Box & Wilson (1951) RSM; risk integration formalized in engineering reliability literature from the 1990s onward | G. G. Vining and Raymond H. Myers (dual response formulation) |
| Тип≠ | Experimental optimization with probabilistic risk constraints | Experimental optimization technique |
| Основополагащ източник≠ | Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2009). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (3rd ed.). Wiley. ISBN: 978-0470174463 | Vining, G. G., & Myers, R. H. (1990). Combining Taguchi and response surface philosophies: A dual response approach. Journal of Quality Technology, 22(1), 38–45. DOI ↗ |
| Други названия | Risk-based RSM, reliability-based RSM, probabilistic RSM, risk-integrated response surface methodology | Robust RSM, dual response surface methodology, robust parameter design via RSM, mean-variance RSM |
| Свързани≠ | 5 | 3 |
| Резюме≠ | Risk-based Response Surface Methodology (Risk-based RSM) extends classical RSM by embedding probabilistic risk or reliability constraints into the experimental optimization process. Rather than seeking a single optimal point under deterministic conditions, it identifies factor settings that achieve performance goals while keeping the probability of failure or unacceptable outcomes below a specified threshold — making it especially valuable in safety-critical and high-variability engineering contexts. | Robust Response Surface Methodology (Robust RSM) is an experimental optimization strategy that simultaneously fits two regression models — one for the mean response and one for its variance (or standard deviation) — across a designed experiment. By jointly optimizing these dual surfaces, engineers identify factor settings that hit a performance target while minimizing process variability, combining the empirical model-building power of classical RSM with the variance-reduction goals of robust parameter design. |
| ScholarGateНабор от данни ↗ |
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