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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
References
- 1. (1995). ‘Computer aided analysis of a surface roughness model for end milling’. Journal of Materials Processing Technology. 55, 2, 123-127 Google Scholar
- 2. (2002). ‘Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments’. Robotics and Computer Integrated Manufacturing. 18, 5–6, 343-354 Google Scholar
- 3. (1997). ‘A dynamic surface roughness model for face milling’. Precision Engineering. 20, 3, 171-178 Google Scholar
- 4. (1995). ‘A proposed statistical model for surface quality prediction in end milling of Al alloy’. International Journal of Machine Tools & Manufacture. 35, 8, 1187-1200 Google Scholar
- 5. (1993). ‘ANFIS: adaptive-network-based fuzzy inference system’. IEEE Trans. Syst. Man Cybernet.. 23, 3, 665-685 Google Scholar
- 6. (1999). ‘Texture prediction of milled surfaces using texture superposition method’. Computer Aided Design. 31, 8, 485-494 Google Scholar
- 7. (1999). ‘Surface roughness prediction technique for CNC end-milling’. Journal of Industrial Technology. 15, 1, 1-6 Google Scholar
- 8. (2002). ‘Surface roughness model for end milling: a semi-free cutting carbon case hardening steel (En32) in dry condition’. Journal of Materials Processing Technology. 124, 1–2, 183-191 Google Scholar
- 9. (1995). Fuzzy Logic with Engineering Applications. New York:McGraw-Hill Inc. Google Scholar
- 10. (1985). ‘Fuzzy identification of systems and its applications to modeling and control’. IEEE Trans. Syst. Man Cybernet.. SMC-15, 1, 116-132 Google Scholar
- 11. (1999). ‘An in-process surface recognition system based on neural networks in end milling cutting operations’. International Journal of Machine Tools & Manufacture. 39, 4, 583-605 Google Scholar
- 12. (1984). ‘On the estimation of parameters for a non-linear model of a milling process’. International Journal of Production Research. 22, 2, 247-252 Google Scholar
