Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Daudzparametru vadības karte× | Vairākuzdevumu atbildes virsmas metodoloģija× | |
|---|---|---|
| Nozare | Eksperimentu plānošana | Eksperimentu plānošana |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 1947 (Hotelling T²); 1980s–1990s (MEWMA, MCUSUM extensions) | 1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson) |
| Autors≠ | Harold Hotelling (multivariate foundation); extended by Lowry, Woodall, and others | Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework) |
| Tips≠ | Multivariate statistical process monitoring | Experimental optimization technique |
| Pirmavots≠ | Hotelling, H. (1947). Multivariate quality control illustrated by the air testing of sample bombsights. In C. Eisenhart, M. W. Hastay, & W. A. Wallis (Eds.), Techniques of Statistical Analysis (pp. 111–184). McGraw-Hill. link ↗ | Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. DOI ↗ |
| Citi nosaukumi | multivariate control chart, multi-response SPC, MRCC, multiple-response monitoring chart | Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | A multi-response control chart simultaneously monitors two or more correlated quality characteristics on a single chart, preserving the correlation structure that univariate charts ignore. Built on Hotelling's T² statistic and its time-weighted extensions (MEWMA, MCUSUM), it detects process shifts that would be missed if each response were charted independently. It is the standard tool in manufacturing and service quality when product performance depends on multiple interrelated outputs. | 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. |
| ScholarGateDatu kopa ↗ |
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