Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Анализ на възможностите на многопараметрични процеси× | Многокритериална методология на повърхността на отклика× | |
|---|---|---|
| Област | Планиране на експеримента | Планиране на експеримента |
| Семейство | Process / pipeline | Process / pipeline |
| Година на възникване≠ | 1993–1994 (foundational multivariate indices) | 1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson) |
| Създател≠ | Taam, Subbaiah & Liddy (multivariate capability); Hubele, Shahriari & Cheng (MCpm) | Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework) |
| Тип≠ | Quantitative quality / process assessment method | Experimental optimization technique |
| Основополагащ източник≠ | Taam, W., Subbaiah, P., & Liddy, J. W. (1993). A note on multivariate capability indices. Journal of Applied Statistics, 20(3), 339–351. link ↗ | Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗ |
| Други названия | MRPCA, multivariate process capability, multi-characteristic capability analysis, vector process capability | Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization |
| Свързани | 6 | 6 |
| Резюме≠ | Multi-response process capability analysis extends classical single-response capability indices (Cp, Cpk) to situations where a process must simultaneously satisfy specification limits on two or more correlated quality characteristics. Rather than evaluating each response in isolation, it assesses the joint probability that all characteristics fall within their respective tolerance regions, yielding a more realistic picture of overall process performance in multi-characteristic manufacturing and engineering settings. | Multi-response Response Surface Methodology (MRSM) extends classical RSM to situations where an experiment generates two or more response variables that must be optimized simultaneously. Rather than tuning factor settings for a single output, MRSM fits a separate second-order polynomial model for each response, then combines them — most commonly via Derringer and Suich's desirability function — to find factor settings that satisfy all objectives at once. |
| ScholarGateНабор от данни ↗ |
|
|