Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32451
Development of Predictive Model for Surface Roughness in End Milling of Al-SiCp Metal Matrix Composites using Fuzzy Logic

Authors: M. Chandrasekaran, D. Devarasiddappa


Metal matrix composites have been increasingly used as materials for components in automotive and aerospace industries because of their improved properties compared with non-reinforced alloys. During machining the selection of appropriate machining parameters to produce job for desired surface roughness is of great concern considering the economy of manufacturing process. In this study, a surface roughness prediction model using fuzzy logic is developed for end milling of Al-SiCp metal matrix composite component using carbide end mill cutter. The surface roughness is modeled as a function of spindle speed (N), feed rate (f), depth of cut (d) and the SiCp percentage (S). The predicted values surface roughness is compared with experimental result. The model predicts average percentage error as 4.56% and mean square error as 0.0729. It is observed that surface roughness is most influenced by feed rate, spindle speed and SiC percentage. Depth of cut has least influence.

Keywords: End milling, fuzzy logic, metal matrix composites, surface roughness

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2062


[1] U. Zuperl, F. Cus, M. Milfelner, "Fuzzy control strategy for an adaptive force control in end-milling", Journal of Materials Processing Technology Vol. 164, 2005, pp. 1472-1478.
[2] J. T Lin, D Bhattacharyya, V Kecman, "Multiple regression and neural networks analyses in composites machining", Composites Science and Technology, Vol. 63, 2003, pp. 539-548
[3] D. R Cramer and D. F. Taggart, "Design and manufacture of an affordable advanced composite automotive body structure", Proc. 19th International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium and Exhibition, October 19-23, 2002, pp. 1-12.
[4] M. Chandrasekaran, M. Muralidhar, C. M. Krishna and U.S. Dixit, "Application of soft computing techniques in machining performance prediction and optimization: a literature review", Int J Adv Manuf Technol, Vol. 46, 2010, pp. 445-464
[5] L. A. Zadeh., "Fuzzy sets", Information and Control, Vol. 8, 1965, pp. 338-353
[6] N. R. Abburi and U. S. Dixit, "A knowledge based system for the prediction of surface roughness in turning process", Robotics and Computer Integrated Manufacturing, Vol. 22, 2006, pp. 363-372
[7] T. Rajasekaran, K. Palanikumar and B.K Vinayagam, "Application of fuzzy logic for modeling surface roughness in turning CFRP composites using CBN tool", Prod. Eng. Res. Devel, Vol. 5, 2011, pp. 191-199
[8] Harun Akkus and Ilhan Asilturk, "Predicting surface roughness of AISI 4140 steel in hard turning process through artificial neural network, fuzzy logic and regression models", Scientific Research and Essays, Vol. 6 (13), 2011, pp. 2729-2736
[9] M. K. Pradhan and C. K. Biswas, "Nero -fuzzy and neural network- based prediction of various responses in electrical discharge machining of AISI D2 steel", Int J Adv Manuf Technol, Vol. 50, 2010, pp. 591- 610.doi: 10.1007/s00170-010-2531-8]
[10] J. P. Davim and C. A. Conceicao Antonio, "Optimal drilling of particulate metal matrix composites based on experimental and numerical procedures", International Journal of Machine Tools and Manufacture, Vol. 41, 2001, pp. 21-31.
[11] S. Basavarajappa, G. Chandramohan, M. Prabhu, K. Mukund and M. Ashwin, "Drilling of hydrid metal matrix composites - workpiece surface integrity", International Journal of Machine tools and Manufacture, Vol. 47, 2007, pp. 92-96
[12] R. Arokiadass, K. Palanirajda and N. Alagumoorthi, "Predictive modeling of surface roughness in end milling of Al/SiCp metal matrix composite", Archives of Applied Science Research, Vol. 3(2), 2011, pp. 228-236.
[13] C. C. Tsao and H. Hocheng, "Evaluation of thrust force and surface roughness in drilling composite material using Taguchi analysis and neural network", Journal of material processing technology, Vol. 203, 2008, pp. 342-348
[14] N. Muthukrishan and P.J Davim, "Optimization of machining parameters of Al/SiC -MMC with ANOVA and ANN analysis", Journal of Materials Processing Technology, Vol. 209, 2009 pp. 225-232
[15] P. J. Davim, "Design of optimization of cutting parameters for turning of metal matrix composites based on the orthogonal arrays", Journal of Materials Processing Technology, Vol. 132, 2003 pp. 340-344
[16] K. A. Risbood, U. S. Dixit and A. D. Sahasrabudhe, "Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process", J Matter Process Technol, Vol. 132, 2003 pp. 203-214. doi: 10.1016/s0924-0136(02)00920-2
[17] D. K. Sonar, U. S. Dixit and D. K. Ojha, "The application of radial basis function for predicting the surface roughness in a turning process", Int J Adv Manuf Technol, Vol. 27, 2006, pp. 661-666. doi: 10.1007/s00170- 004-2258-5
[18] Y. M. Ali and L. C. Zhang, "Surface roughness prediction of ground components using a fuzzy logic approach", Journal of Materials Processing Technology, Vol. 89(90), 1999 pp. 561-568.
[19] D. Devarasiddappa, M. Chandrasekaran and A. Mandal, "Artificial neural network modeling for predicting surface roughness in end milling of Al-SiCp metal matrix composite and its evaluation", Proc .International Conference on Intelligent Manufacturing Systems (ICIMS 2012) SASTRA University, Thanjavur, Taminnadu (India), pp 119-125
[20] J. C. Chen, J. T. Black, "A fuzzy-nets in-process (FNIP) systems for too l breakage monitoring in end-milling operations", Int J Mach Tools Manuf, Vol. 37(6), 1997, pp.783-800.