방법 증거 기록

Multi-response Response Surface Methodology

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.

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원본 기록

방법의 원본 기록에서 그대로 복사된 인용입니다. 이로부터 수준별 검증이 추론되지 않습니다.

분류학적 방법 기록 · process-pipeline / experimental-design

Derringer, G., & Suich, R. (1980). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214–219. · DOI 10.1080/00224065.1980.11980968
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

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Taxonomic bucketBox-Behnken Designmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketCentral Composite Designmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketDesign of experimentsmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketOptimization-assisted response surface methodologymachine-suggested · Relational suggestion, not evidence.Taxonomic bucketQuality Function Deploymentmachine-suggested · Relational suggestion, not evidence.Used in the same domainResponse Surface Methodologymachine-suggested · Relational suggestion, not evidence.