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| Analisis Kebolehan Proses Multi-respons× | Metodologi Permukaan Respons Berbilang Respons× | |
|---|---|---|
| Bidang | Reka Bentuk Eksperimen | Reka Bentuk Eksperimen |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | 1993–1994 (foundational multivariate indices) | 1980 (Derringer & Suich desirability function); RSM roots ~1951 (Box & Wilson) |
| Pengasas≠ | Taam, Subbaiah & Liddy (multivariate capability); Hubele, Shahriari & Cheng (MCpm) | Derringer & Suich (desirability function approach); Myers & Montgomery (RSM framework) |
| Jenis≠ | Quantitative quality / process assessment method | Experimental optimization technique |
| Sumber perintis≠ | 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 ↗ |
| Alias | MRPCA, multivariate process capability, multi-characteristic capability analysis, vector process capability | Multi-response RSM, MRSM, Multi-objective RSM, Multiple response optimization |
| Berkaitan | 6 | 6 |
| Ringkasan≠ | 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. |
| ScholarGateSet data ↗ |
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