Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Simulaatioavusteinen vastepintamenetelmä× | Optimointiavusteinen vastepintamenetelmä× | |
|---|---|---|
| Tieteenala | Koesuunnittelu | Koesuunnittelu |
| Menetelmäperhe | Process / pipeline | Process / pipeline |
| Syntyvuosi≠ | 1951 (RSM); simulation integration widely adopted from 1980s onward | 1951 (RSM); 1980 (desirability-function optimization formalized) |
| Kehittäjä≠ | Box & Wilson (RSM foundation); Kleijnen and others for simulation-based extensions | Derringer & Suich (desirability function); Box & Wilson (RSM foundation) |
| Tyyppi≠ | Experimental optimization method | Hybrid experimental-optimization framework |
| Alkuperäislähde≠ | Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley. ISBN: 978-1118916025 | Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗ |
| Rinnakkaisnimet | SA-RSM, simulation-based RSM, computer simulation RSM, metamodel-assisted RSM | OA-RSM, RSM with optimization, desirability-based RSM, multi-response RSM optimization |
| Liittyvät≠ | 6 | 5 |
| Tiivistelmä≠ | Simulation-assisted response surface methodology (SA-RSM) combines computer simulation models — such as finite element analysis, computational fluid dynamics, or discrete-event simulation — with the statistical framework of response surface methodology to efficiently map, model, and optimize system responses. Instead of running physical experiments, the researcher executes simulation runs at design points prescribed by an RSM design, fits a polynomial metamodel (surrogate) to the simulation outputs, and uses that metamodel to locate optimal factor settings. | Optimization-assisted RSM couples a second-order response surface model with a mathematical optimization routine — most commonly Derringer and Suich's desirability function, but also genetic algorithms or gradient-based solvers — to locate the factor settings that simultaneously satisfy multiple quality or performance objectives. The result is a data-driven recommendation for optimal process or product conditions, supported by a polynomial model fitted to a structured experimental design. |
| ScholarGateAineisto ↗ |
|
|