A Review: An insight approach for Optimization of machining parameters using Response Surface Methodology and Grey Relational Analysis

This paper gives you insight about the two methodologies namely response surface methodology (RSM) and Grey relational analysis (GRA). Both the methodologies are used for optimization of machining parameters but RSM has an edge over GRA in the fact that in can locate optimal space better. RSM has its own design for output response model formulation(example MRR and surface roughness) based on input parameters and comparing the predicted values of models with the confirmation runs while GRA experimental design based on Taguchi orthogonal array and normalising the response values by performing certain sequence of steps and then comparing the predicted value with confirmation runs. For example if there are three input parameters (example speed, feed and depth of cut) and each has three levels so total possible combination is twenty seven. So the difference lies in the fact that GRA will find an optimised input parameter based on predetermined levels but RSM will explore the optimal region more effectively and can find the optimised value other then taken levels in the range. So a comparison between two methodologies can help in better optimising the machining parameters.