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| Risikobaseret Response Surface Methodology× | Responsflademetodologi (RSM)× | |
|---|---|---|
| Fagområde | Forsøgsdesign | Forsøgsdesign |
| Familie≠ | Process / pipeline | Hypothesis test |
| Oprindelsesår≠ | 1990s–2000s (risk-based extensions) | 1951 |
| Ophavsperson≠ | Builds on Box & Wilson (1951) RSM; risk integration formalized in engineering reliability literature from the 1990s onward | George E. P. Box & K. B. Wilson |
| Type≠ | Experimental optimization with probabilistic risk constraints | Second-order polynomial response surface model |
| Oprindelig kilde≠ | 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 | 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 ↗ |
| Aliasser≠ | Risk-based RSM, reliability-based RSM, probabilistic RSM, risk-integrated response surface methodology | RSM, Central Composite Design, Box-Behnken Design, CCD |
| Relaterede≠ | 5 | 7 |
| Resumé≠ | 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. | 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. |
| ScholarGateDatasæt ↗ |
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