ANFIS Modeling of Surface Roughness in Milling Operation of 316L Stainless Steels

Stainless steel is a material that can be used in many areas because it resists corrosion excellently, can be used at low and high temperatures, is easy to shape and has a pleasant aestheticappearance. In this study, a model has been developed using the adaptive network based fuzzy logic inference system (ANFIS) approach based on the surface roughness cutting parameters of 316L stainless steel. Cutting speed, feed, cutting depth and cutting width are selected as cutting parameters. ANFIS modeling was performed using the ANFIS editor of the Matlab 8.5 program. When the experimental values were compared with the predicted values of the developed ANFIS model, it was found that the maximum percentage error value was 9.58 and the average percentage error value was 5.25. The correlation coefficient of the ANFIS model was 0.997. The results showed that ANFIS can be an effective method for estimating surface roughness in 316L stainless steel milling process