A genetic algorithmic approach for optimization of surface roughness prediction model

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A review of machining monitoring systems based on artificial intelligence process models (2010, methodology): “[ 71 ] s :3 ,f :3 ,d :3 ,r: 3 Response surface Ra prediction in turning modelling”
Predicting surface roughness in machining: a review (2003, methodology): “[41] also adopted a two stage approach towards optimizing for surface roughness.”
Selection of optimum conditions for maximum material removal rate with surface finish and damage as constraints in SiC grinding (2003, methodology): “[14] have developed a surface roughness prediction model for turning mild steel using response surface methodology to give the factor effects of the individual process parameters.”
Surface Roughness Analysis in Machining of Titanium Alloy (2008, methodology): “[11] have used response surface method and genetic algorithm for predicting the surface roughness and optimizing the process parameters.”
Diamond machining of silicon: A review of advances in molecular dynamics simulation (2015, neutral): “Rmax = 6 nm [14], which is better than that obtained through grinding, i.”
Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling (2008, methodology): “Suresh [ 13 ] used the response surface method and genetic algorithm for predicting the surface roughness and optimizing the process parameters.”