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Sensitivity analysis-integrated response surface methodology

Sensitivity analysis-integrated RSM couples a structured experimental design with a formal sensitivity analysis of the fitted response surface model. After estimating a polynomial surrogate from designed experiments, global or local sensitivity indices are computed to quantify each input factor's relative contribution to output variability. This allows practitioners to identify which factors truly drive the response before committing to full optimization, reducing cost and improving the reliability of the final optimum.

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Sensitivity Analysis-Integrated Response Surface Methodology

Taksonomisk metoderegister · process-pipeline / experimental-design

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 978-1118916018
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. · ISBN 978-0470059975

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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.Used in the same domainResponse Surface Methodologymachine-suggested · Relational suggestion, not evidence.