Response Surface Methodology - A Review

Response Surface Methodology (RSM) is a technique which involves some statistical and mathematical calculations and helps developing, improving and optimizing product and process. Although various optimization methods are available in literature such as one-factorial method, Taguchi design of experiments etc., RSM has its own benefits when compared to other conventional techniques of optimization. The main drawback of one-factorial method lies in is lengthy and time consuming process. Also in one-factorial it is not possible to detect parameter interactive impact on response. Taguchi approach of optimization is very familiar and popular in the field optimization which minimizes the number of experiments by using the concept of Design of Experiments. It uses the concept of signal-to-noise ratio and analysis of variance for finding the optimal parameter combination and its effect on the response. But in Taguchi method the results cannot be fitted in regression equation for locating the optimum level. In this perspective, RSM is best suitable and sufficient method for multiple variable optimizations to find the optimal conditions with minimum number of experiments. This paper reviews the concept of RSM and previously conducted experiments which got published in international journals. Although, lot of literature is available on optimization methods, there is no specific literature written on RSM. In this study a total of 27 RSM papers were reviewed which got published between 2000 and 2018 in various reputed international journals.