Title: Modelling and prediction of surface roughness in end milling operation using adaptive neuro-fuzzy inference system

Authors: Shibendu Shekhar Roy

Addresses: Department of Mechanical Engineering, National Institute of Technology, Durgapur-713209, W.B., India

Abstract: Surface roughness is a widely used index of product quality and the quality of the surface plays a very important role in the performance of the milling operation, as a good quality milled surface significantly improves fatigue strength and wear resistance. Therefore, modelling and prediction of surface roughness of a workpiece in milling operation plays an important role in manufacturing industry. This paper proposes an adaptive neuro-fuzzy inference system (ANFIS) for modelling and predicting the surface roughness in end milling. Three cutting parameters, i.e., spindle speed, feed rate and depth of cut, those have a major impact on surface roughness were analysed. Three different membership functions namely, triangular, trapezoidal and bell-shaped, were used during the hybrid-training process of ANFIS in order to compare the prediction accuracy of surface roughness by the two membership functions. The predicted surface roughness values obtained from ANFIS were compared with experimental data and multiple regression analysis. The comparison indicates that the adoption of above three membership functions in ANFIS achieved much better accuracy than multiple regression model. The bell-shaped membership function in ANFIS achieves slightly higher prediction accuracy than other membership functions.

Keywords: surface roughness; modelling; prediction; adaptive neuro-fuzzy inference system; ANFIS; end milling; surface quality; product quality; neural networks; fuzzy logic; spindle speed; feed rate; depth of cut.

DOI: 10.1504/IJESMS.2012.048662

International Journal of Engineering Systems Modelling and Simulation, 2012 Vol.4 No.3, pp.145 - 154

Published online: 30 Aug 2014 *

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