A Fuzzy Logic Based Model to Predict Surface Roughness of A Machined Surface in Glass Milling Operation Using CBN Grinding Tool
Nowadays, the demand for high product quality
focuses extensive attention to the quality of machined surface. The
(CNC) milling machine facilities provides a wide variety of
parameters set-up, making the machining process on the glass
excellent in manufacturing complicated special products compared to
other machining processes. However, the application of grinding
process on the CNC milling machine could be an ideal solution to
improve the product quality, but adopting the right machining
parameters is required. In glass milling operation, several machining
parameters are considered to be significant in affecting surface
roughness. These parameters include the lubrication pressure, spindle
speed, feed rate and depth of cut. In this research work, a fuzzy logic
model is offered to predict the surface roughness of a machined
surface in glass milling operation using CBN grinding tool. Four
membership functions are allocated to be connected with each input
of the model. The predicted results achieved via fuzzy logic model
are compared to the experimental result. The result demonstrated
settlement between the fuzzy model and experimental results with the
93.103% accuracy.
[1] L, B.E., Glass: Mechanics and Technology. 2007: Wiley-VCH.
[2] R, D. and N. Y, Handbook of semiconductor manufacturing technology.
2007: CRC Press.
[3] L, Y. and H. H, Brittle materials in nano-abrasive fabrication of optical
mirror-surface. Precision Engineering, 2008. 32: p. 336 - 341.
[4] W, Z.Z. and L.W. Y, Grinding of silicone and glass using a new dressing
device and an improved lubrication system. Journal of Material and
Manufacturing Processes, 2001. 16(4): p. 471-482.
[5] B, Z., Helical scan grinding of brittle and ductile materials. Journal of
Materials Processing Technology, 1999. 91: p. 196-205.
[6] H, T.Y., C.J. C, and L.S. J, In-process surface recognition system based
on neural networks in end milling cutting operations. Int. J. Mach. Tool
Manuf., 1999. 39(4): p. 583-605.
[7] M, G., P. B, Coutris N, C.B. A, F. C, P. P, T.D. W, and B.K. D, A
simple ballistic material model for soda-lime glass. International Journal
of Impact Engineering, 2009. 36: p. 386-401.
[8] W, Z.Z., Surface finish of precision machined advanced materials.
Journal of Material Processing Technology, 2002. 122: p. 173-178.
[9] Sayuti, M., Ahmed A.D. Sarhan, M. Fadzil, and M. Hamdi,
Enhancement and verification of a machined surface quality for glass
milling operation-using CBN grinding tool- Taguchi approach.
International Journal of Advanced Manufacturing Technology, 2011.
[10] Zhang, J.Z., J.C. Chen, and E.D. Kirby, Surface roughness optimization
in an end-milling operation using the Taguchi design method. Journal of
Materials Processing Technology, 2007. 184(1-3): p. 233-239.
[11] Ghani, J.A., I.A. Choudhury, and H.H. Hassan, Application of Taguchi
method in the optimization of end milling parameters. Journal of
Materials Processing Technology, 2004. 145(1): p. 84-92.
[12] Zalnezhad, E., Ahmed A.D. Sarhan, and M. Hamdi, Optimizing the
PVD TiN Thin Film Coating's parameters on Aerospace AL7075-T6
Alloy for Higher Coating Hardness and Adhesion with Better
Tribological Properties of the Coating Surface. International Journal of
Advanced Manufacturing Technology.
[13] Sayuti, M., Ahmed A.D. Sarhan, and M. Hamdi, Optimizing the
Machining Parameters in Glass Grinding Operation on the CNC Milling
Machine for Best Surface Roughness. Advance Material Research. ,
2011. 154-155: p. 721-726.
[14] H, H., Machining characteristics and surface integrity of yytria stabilized
tetragonal zirconia in high deep grinding. Material Science and
Engineering, 2003. 345: p. 155 - 163.
[15] B, Y., S. X, and L. S, Mechanisms of edge chipping in laser-assisted
milling of silicon nitride ceramics. International Journal of Machine
Tools and Manufacture, 2009. 49: p. 344-350.
[16] Sarhan, A., R. Sayed, A.A. Nassr, and R.M. El-Zahry, Interrelationships
between cutting force variation and tool wear in end-milling. Journal of
Materials Processing Technology, 2001. 109: p. 229-235.
[17] Ahmed A.D. Sarhan, M.Sayuti, and M. Hamdi, Reduction of power and
lubricant oil consumption in milling process using a new SiO2
nanolubrication system. International Journal of Advanced
Manufacturing Technology, 2011 p. DOI: 10.1007/s00170-012-3940-7.
[18] Sonar, D.K., U.S. Dixit, and D.K. Ojha, The application of a radial basis
function neural network for predicting the surface roughness in a turning
process. The International Journal of Advanced Manufacturing
Technology, 2006. 27(7): p. 661-666.
[19] Shamshirband, S., S. Kalantari, and Z. Bakhshandeh, Designing a smart
multi-agent system based on fuzzy logic to improve the gas consumption
pattern. Scientific Research and Essays 2010. Vol. 5(6): p. 592-605.
[20] Jaya, A.S.M., S.Z.M. Hashim, and M.N.A. Rahman. Fuzzy logic-based
for predicting roughness performance of TiAlN coating. in Intelligent
Systems Design and Applications (ISDA), 2010 10th International
Conference on. 2010.
[21] Leung, R.W.K., H.C.W. Lau, and C.K. Kwong, An expert system to
support the optimization of ion plating process: an OLAP-based fuzzycum-
GA approach. Expert Systems with Applications, 2003. 25(3): p.
313-330.
[22] Oktem, H., T. Erzurumlu, and F. Erzincanli, Prediction of minimum
surface roughness in end milling mold parts using neural network and
genetic algorithm. Materials & Design, 2006. 27(9): p. 735-744.
[23] Chandrasekaran, M., M. Muralidhar, C. Krishna, and U. Dixit,
Application of soft computing techniques in machining performance
prediction and optimization: a literature review. The International
Journal of Advanced Manufacturing Technology, 2010. 46(5): p. 445-
464.
[24] Hashmi, K., I.D. Graham, and B. Mills, Data selection for turning
carbon steel using a fuzzy logic approach. Journal of Materials
Processing Technology, 2003. 135(1): p. 44-58.
[1] L, B.E., Glass: Mechanics and Technology. 2007: Wiley-VCH.
[2] R, D. and N. Y, Handbook of semiconductor manufacturing technology.
2007: CRC Press.
[3] L, Y. and H. H, Brittle materials in nano-abrasive fabrication of optical
mirror-surface. Precision Engineering, 2008. 32: p. 336 - 341.
[4] W, Z.Z. and L.W. Y, Grinding of silicone and glass using a new dressing
device and an improved lubrication system. Journal of Material and
Manufacturing Processes, 2001. 16(4): p. 471-482.
[5] B, Z., Helical scan grinding of brittle and ductile materials. Journal of
Materials Processing Technology, 1999. 91: p. 196-205.
[6] H, T.Y., C.J. C, and L.S. J, In-process surface recognition system based
on neural networks in end milling cutting operations. Int. J. Mach. Tool
Manuf., 1999. 39(4): p. 583-605.
[7] M, G., P. B, Coutris N, C.B. A, F. C, P. P, T.D. W, and B.K. D, A
simple ballistic material model for soda-lime glass. International Journal
of Impact Engineering, 2009. 36: p. 386-401.
[8] W, Z.Z., Surface finish of precision machined advanced materials.
Journal of Material Processing Technology, 2002. 122: p. 173-178.
[9] Sayuti, M., Ahmed A.D. Sarhan, M. Fadzil, and M. Hamdi,
Enhancement and verification of a machined surface quality for glass
milling operation-using CBN grinding tool- Taguchi approach.
International Journal of Advanced Manufacturing Technology, 2011.
[10] Zhang, J.Z., J.C. Chen, and E.D. Kirby, Surface roughness optimization
in an end-milling operation using the Taguchi design method. Journal of
Materials Processing Technology, 2007. 184(1-3): p. 233-239.
[11] Ghani, J.A., I.A. Choudhury, and H.H. Hassan, Application of Taguchi
method in the optimization of end milling parameters. Journal of
Materials Processing Technology, 2004. 145(1): p. 84-92.
[12] Zalnezhad, E., Ahmed A.D. Sarhan, and M. Hamdi, Optimizing the
PVD TiN Thin Film Coating's parameters on Aerospace AL7075-T6
Alloy for Higher Coating Hardness and Adhesion with Better
Tribological Properties of the Coating Surface. International Journal of
Advanced Manufacturing Technology.
[13] Sayuti, M., Ahmed A.D. Sarhan, and M. Hamdi, Optimizing the
Machining Parameters in Glass Grinding Operation on the CNC Milling
Machine for Best Surface Roughness. Advance Material Research. ,
2011. 154-155: p. 721-726.
[14] H, H., Machining characteristics and surface integrity of yytria stabilized
tetragonal zirconia in high deep grinding. Material Science and
Engineering, 2003. 345: p. 155 - 163.
[15] B, Y., S. X, and L. S, Mechanisms of edge chipping in laser-assisted
milling of silicon nitride ceramics. International Journal of Machine
Tools and Manufacture, 2009. 49: p. 344-350.
[16] Sarhan, A., R. Sayed, A.A. Nassr, and R.M. El-Zahry, Interrelationships
between cutting force variation and tool wear in end-milling. Journal of
Materials Processing Technology, 2001. 109: p. 229-235.
[17] Ahmed A.D. Sarhan, M.Sayuti, and M. Hamdi, Reduction of power and
lubricant oil consumption in milling process using a new SiO2
nanolubrication system. International Journal of Advanced
Manufacturing Technology, 2011 p. DOI: 10.1007/s00170-012-3940-7.
[18] Sonar, D.K., U.S. Dixit, and D.K. Ojha, The application of a radial basis
function neural network for predicting the surface roughness in a turning
process. The International Journal of Advanced Manufacturing
Technology, 2006. 27(7): p. 661-666.
[19] Shamshirband, S., S. Kalantari, and Z. Bakhshandeh, Designing a smart
multi-agent system based on fuzzy logic to improve the gas consumption
pattern. Scientific Research and Essays 2010. Vol. 5(6): p. 592-605.
[20] Jaya, A.S.M., S.Z.M. Hashim, and M.N.A. Rahman. Fuzzy logic-based
for predicting roughness performance of TiAlN coating. in Intelligent
Systems Design and Applications (ISDA), 2010 10th International
Conference on. 2010.
[21] Leung, R.W.K., H.C.W. Lau, and C.K. Kwong, An expert system to
support the optimization of ion plating process: an OLAP-based fuzzycum-
GA approach. Expert Systems with Applications, 2003. 25(3): p.
313-330.
[22] Oktem, H., T. Erzurumlu, and F. Erzincanli, Prediction of minimum
surface roughness in end milling mold parts using neural network and
genetic algorithm. Materials & Design, 2006. 27(9): p. 735-744.
[23] Chandrasekaran, M., M. Muralidhar, C. Krishna, and U. Dixit,
Application of soft computing techniques in machining performance
prediction and optimization: a literature review. The International
Journal of Advanced Manufacturing Technology, 2010. 46(5): p. 445-
464.
[24] Hashmi, K., I.D. Graham, and B. Mills, Data selection for turning
carbon steel using a fuzzy logic approach. Journal of Materials
Processing Technology, 2003. 135(1): p. 44-58.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:53420", author = "Ahmed A. D. Sarhan and M. Sayuti and M. Hamdi", title = "A Fuzzy Logic Based Model to Predict Surface Roughness of A Machined Surface in Glass Milling Operation Using CBN Grinding Tool", abstract = "Nowadays, the demand for high product quality
focuses extensive attention to the quality of machined surface. The
(CNC) milling machine facilities provides a wide variety of
parameters set-up, making the machining process on the glass
excellent in manufacturing complicated special products compared to
other machining processes. However, the application of grinding
process on the CNC milling machine could be an ideal solution to
improve the product quality, but adopting the right machining
parameters is required. In glass milling operation, several machining
parameters are considered to be significant in affecting surface
roughness. These parameters include the lubrication pressure, spindle
speed, feed rate and depth of cut. In this research work, a fuzzy logic
model is offered to predict the surface roughness of a machined
surface in glass milling operation using CBN grinding tool. Four
membership functions are allocated to be connected with each input
of the model. The predicted results achieved via fuzzy logic model
are compared to the experimental result. The result demonstrated
settlement between the fuzzy model and experimental results with the
93.103% accuracy.", keywords = "CNC-machine, Glass milling, Grinding, Surface
roughness, Cutting force, Fuzzy logic model.", volume = "6", number = "10", pages = "2119-7", }
