Process / pipelineDesign of experiments
Response Surface Methodology with Desirability Function Optimization
Response Surface Methodology (RSM) is a set of statistical and mathematical techniques for modeling and optimizing processes with multiple inputs (factors) and outputs (responses). The Desirability Function approach, introduced by Harrington (1965) and refined by Derringer and Suich (1980), extends RSM to solve multi-response optimization problems by combining competing objectives into a single index. This methodology is essential in product and process development where engineers must balance performance, cost, and reliability.
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Sources
- Box, G. E. P., & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, 13(1), 1-45. DOI: 10.1111/j.2517-6161.1951.tb00046.x ↗
- Harrington, E. C. (1965). The desirability function. Journal of Quality Technology, 4(6), 494-509. link ↗
- 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 (3rd ed.). Wiley. DOI: 10.1002/9781118188607 ↗