Linganisha mbinu

	Mbinu ya Uso wa Mwitikio wa Maombi ya Viwandani	Mbinu ya uso wa mwitikio (RSM)
Nyanja	Muundo wa Majaribio	Muundo wa Majaribio
Familia	Process / pipeline	Hypothesis test
Mwaka wa asili	1951 (origin); widespread industrial adoption from 1980s onward	1951
Mwanzilishi	George E. P. Box & K. B. Wilson; industrialized by Douglas Montgomery and colleagues	George E. P. Box & K. B. Wilson
Aina	Empirical optimization technique	Second-order polynomial response surface model
Chanzo asilia	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.
Majina mbadala	Industrial RSM, RSM for manufacturing, process optimization RSM, industrial response surface analysis	RSM, Central Composite Design, Box-Behnken Design, CCD
Zinazohusiana	5	7
Muhtasari	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.