An appliance of adaptive Neuro-Fuzzy inference system for predicting the surface roughness of Al-4.5%Cu-TiC MMC in turning operation of CNC milling

Abstract

In the recent decade, due to extensive application of computerized machine tools in the manufacturing industries, manufacturing processes are in need of highly reliable predictive model for the stable machining. Surface roughness(SR) essentially affects the strength of the developed parts, thus in order to attain high quality and productivity in CNC milling process prediction of surface roughness is very essential as it plays an imperative role in this sector. Hence in this present investigation, an effort has been given to predict the surface roughness of Al-4.5%Cu-TiC MMC, while turning in CNC milling by the use of Adaptive Neuro-Fuzzy Inference System (ANFIS). The experimentation is based on Taguchi’s L25 orthogonal array with the significant parameters such as cutting speed, depth of cut and feed rate. Using the experimental results, an ANFIS model is developed for prediction of surface roughness. The Gaussian fuzzy membership function is adopted in this present investigation for the analysis of ANFIS training as it gives better accuracy. Simulation results are validated through a number of trial cases and the results obtained by training of ANFIS are compared with the results obtained by regression analysis. Comparison result shows that the ANFIS results are better than the results obtained by regression model.