Neural Network and Fuzzy Logic based prediction of Surface Roughness and MRR in Cylindrical Grinding Process

Surface roughness and metal removal rate are the prominent output responses that influence the quality and cost of production. Moreover, in this present investigation neural network modeling and Adaptive-neuro fuzzy inference system (ANFIS) are employed to predict the output responses with respect to a variety of cutting parameters in cylindrical grinding process. A full factorial experimental design is conducted on AISI 1040 steel; considering work speed, depth of cut and feed rate as the cutting parameters. In addition 8 sets of experiments are performed to validate the computational methods. Our comparative study on identifying a better prediction methodology illustrated ANFIS as a better technique with 91% accuracy, while depth of cut as the most influential parameter effecting output responses. (C) 2017 Published by Elsevier Ltd.