Comparar métodos

	Metodología de Superficie de Respuesta para Aplicaciones Industriales	Metodología de Superficie de Respuesta (RSM)
Campo	Diseño experimental	Diseño experimental
Familia	Process / pipeline	Hypothesis test
Año de origen	1951 (origin); widespread industrial adoption from 1980s onward	1951
Autor original	George E. P. Box & K. B. Wilson; industrialized by Douglas Montgomery and colleagues	George E. P. Box & K. B. Wilson
Tipo	Empirical optimization technique	Second-order polynomial response surface model
Fuente seminal	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	Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45.
Alias	Industrial RSM, RSM for manufacturing, process optimization RSM, industrial response surface analysis	RSM, Central Composite Design, Box-Behnken Design, CCD
Relacionados	5	7
Resumen	Industrial Applications Response Surface Methodology (RSM) applies the classical Box-Wilson response surface framework to manufacturing and process engineering problems. It builds an empirical polynomial model linking controllable process inputs — such as temperature, pressure, feed rate, or catalyst concentration — to one or more quality responses, then mathematically locates the input settings that optimize those responses. It is the de-facto standard statistical tool for process characterization and optimization in chemical, mechanical, food, materials, and pharmaceutical manufacturing.	Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics.