Prediction of Optimum Cutting Parameters to obtain Desired Surface in Finish Pass end Milling of Aluminium Alloy with Carbide Tool using Artificial Neural Network
End milling process is one of the common metal
cutting operations used for machining parts in manufacturing
industry. It is usually performed at the final stage in manufacturing a
product and surface roughness of the produced job plays an
important role. In general, the surface roughness affects wear
resistance, ductility, tensile, fatigue strength, etc., for machined parts
and cannot be neglected in design. In the present work an
experimental investigation of end milling of aluminium alloy with
carbide tool is carried out and the effect of different cutting
parameters on the response are studied with three-dimensional
surface plots. An artificial neural network (ANN) is used to establish
the relationship between the surface roughness and the input cutting
parameters (i.e., spindle speed, feed, and depth of cut). The Matlab
ANN toolbox works on feed forward back propagation algorithm is
used for modeling purpose. 3-12-1 network structure having
minimum average prediction error found as best network architecture
for predicting surface roughness value. The network predicts surface
roughness for unseen data and found that the result/prediction is
better. For desired surface finish of the component to be produced
there are many different combination of cutting parameters are
available. The optimum cutting parameter for obtaining desired
surface finish, to maximize tool life is predicted. The methodology is
demonstrated, number of problems are solved and algorithm is coded
in MatlabĀ®.
[1] P. G. Benardos, and G. C. Vosniakos, "Prediction of surface roughness
in CNC face milling using neural networks and Taguchi-s design of
experiments", Robot and Computer Integrat. Manuf., vol. 18, pp. 343-
354.
[2] M. S. J. Hasmi, "Optimization of surface finish in end milling inconel
718", J. Mater. Process Technol., vol. 56, pp. 54-65, 1996.
[3] M. Alauddin, M. A. El Baradie, and M. S. J. Hashmi, "Computer-aided
analysis of a surface roughness model for end milling", J. Mater.
Process Technol.,vol. 7, pp. 55:123, 1995.
[4] N. Reddy, and P. V. Rao, "Selection of optimum tool geometry and
cutting conditions using a surface roughness prediction model for end
milling", Int. J. Adv. Manuf. Technol., vol. 26, pp. 1202-1210, 2005.
[5] S. J. Lou, and J. C. Chen, "In-process surface roughness
recognition(ISRR) system in end-milling operation", Int. J. Adv. Manuf.
Technol., vol. 9, pp. 15:200, 1999.
[6] S. T. Chiang, D. I. Liu, A. C. Lee, and W. H. Chieng, "Adaptive
controloptimization in end milling using neural networks", Int. J. Mach.
Tools Manuf., vol. 35, no. 4, pp. 637-60, 1995.
[7] T. Luo, W. Lu, K. Krishnamurthy, and B. McMillin, "Neural network
approach for force and contour error control in multi-dimensional end
milling operations", Int. J. Mach. Tools Manuf., vol. 59, pp. 38:1343,
1998.
[8] S. S. Rangwala, and D. A. Dornfeld, "Learning and optimization of
machining operations using computing abilities of neural networks",
IEEE Trans. Syst. Man. Cybern., vol. 19, pp. 299-317, 1989.
[9] A. Kohli, and U. S. Dixit, "A neural network based methodology for
prediction of surface roughness in turning process", International
Journal of Advanced Manufacturing Technology, vol. 25 no. 1-2, pp.
118-129, 2005.
[10] A. M. Zain, H. Haron, and S. Sharif, "Prediction of surface roughness in
the end milling machining using Artificial Neural Network", Expert
Systems with Applications, vol. 37, pp. 1755-1768, 2010.
[11] A. Aggarwal, and H. Singh, "Optimization of machining techniques - A
retrospective and literature review", Sadhana, vol. 30, no. 6, pp. 699-
711, , 2005.
[12] I. Mukherjee, and P. K. Ray, "A review of optimization techniques in
metal cutting processes", Computers & Industrial Engineering, vol. 50,
no. 1-2, pp. 15-34, 2006.
[13] 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. of Adv. Manuf.
Technol, vol. 46, no. 5-8, pp. 445-464, 2010.
[14] F. M. Ham, and I. Kostanic, Principles of neuro computing for science
and engineering. McGraw-Hill, New York, 2001.
[15] M. Tolouei-Rad, and I. M. Bidhendi, "On the optimization of machining
parameters for milling operations", Int. J. Mach. Tools Manuf., vol. 37,
no. 1, pp. 1-16, 1997.
[16] P. Palanisamy, I. Rajendran, and S. Shanmugasundaram, "Optimization
of machining parameters ueing genetic algorithm and experimental
validation for end-milling operations", Int. J. Adv. Manu. Techno., vol.
32, pp. 644-655, 2007.
[1] P. G. Benardos, and G. C. Vosniakos, "Prediction of surface roughness
in CNC face milling using neural networks and Taguchi-s design of
experiments", Robot and Computer Integrat. Manuf., vol. 18, pp. 343-
354.
[2] M. S. J. Hasmi, "Optimization of surface finish in end milling inconel
718", J. Mater. Process Technol., vol. 56, pp. 54-65, 1996.
[3] M. Alauddin, M. A. El Baradie, and M. S. J. Hashmi, "Computer-aided
analysis of a surface roughness model for end milling", J. Mater.
Process Technol.,vol. 7, pp. 55:123, 1995.
[4] N. Reddy, and P. V. Rao, "Selection of optimum tool geometry and
cutting conditions using a surface roughness prediction model for end
milling", Int. J. Adv. Manuf. Technol., vol. 26, pp. 1202-1210, 2005.
[5] S. J. Lou, and J. C. Chen, "In-process surface roughness
recognition(ISRR) system in end-milling operation", Int. J. Adv. Manuf.
Technol., vol. 9, pp. 15:200, 1999.
[6] S. T. Chiang, D. I. Liu, A. C. Lee, and W. H. Chieng, "Adaptive
controloptimization in end milling using neural networks", Int. J. Mach.
Tools Manuf., vol. 35, no. 4, pp. 637-60, 1995.
[7] T. Luo, W. Lu, K. Krishnamurthy, and B. McMillin, "Neural network
approach for force and contour error control in multi-dimensional end
milling operations", Int. J. Mach. Tools Manuf., vol. 59, pp. 38:1343,
1998.
[8] S. S. Rangwala, and D. A. Dornfeld, "Learning and optimization of
machining operations using computing abilities of neural networks",
IEEE Trans. Syst. Man. Cybern., vol. 19, pp. 299-317, 1989.
[9] A. Kohli, and U. S. Dixit, "A neural network based methodology for
prediction of surface roughness in turning process", International
Journal of Advanced Manufacturing Technology, vol. 25 no. 1-2, pp.
118-129, 2005.
[10] A. M. Zain, H. Haron, and S. Sharif, "Prediction of surface roughness in
the end milling machining using Artificial Neural Network", Expert
Systems with Applications, vol. 37, pp. 1755-1768, 2010.
[11] A. Aggarwal, and H. Singh, "Optimization of machining techniques - A
retrospective and literature review", Sadhana, vol. 30, no. 6, pp. 699-
711, , 2005.
[12] I. Mukherjee, and P. K. Ray, "A review of optimization techniques in
metal cutting processes", Computers & Industrial Engineering, vol. 50,
no. 1-2, pp. 15-34, 2006.
[13] 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. of Adv. Manuf.
Technol, vol. 46, no. 5-8, pp. 445-464, 2010.
[14] F. M. Ham, and I. Kostanic, Principles of neuro computing for science
and engineering. McGraw-Hill, New York, 2001.
[15] M. Tolouei-Rad, and I. M. Bidhendi, "On the optimization of machining
parameters for milling operations", Int. J. Mach. Tools Manuf., vol. 37,
no. 1, pp. 1-16, 1997.
[16] P. Palanisamy, I. Rajendran, and S. Shanmugasundaram, "Optimization
of machining parameters ueing genetic algorithm and experimental
validation for end-milling operations", Int. J. Adv. Manu. Techno., vol.
32, pp. 644-655, 2007.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:55706", author = "Anjan Kumar Kakati and M. Chandrasekaran and Amitava Mandal and Amit Kumar Singh", title = "Prediction of Optimum Cutting Parameters to obtain Desired Surface in Finish Pass end Milling of Aluminium Alloy with Carbide Tool using Artificial Neural Network", abstract = "End milling process is one of the common metal
cutting operations used for machining parts in manufacturing
industry. It is usually performed at the final stage in manufacturing a
product and surface roughness of the produced job plays an
important role. In general, the surface roughness affects wear
resistance, ductility, tensile, fatigue strength, etc., for machined parts
and cannot be neglected in design. In the present work an
experimental investigation of end milling of aluminium alloy with
carbide tool is carried out and the effect of different cutting
parameters on the response are studied with three-dimensional
surface plots. An artificial neural network (ANN) is used to establish
the relationship between the surface roughness and the input cutting
parameters (i.e., spindle speed, feed, and depth of cut). The Matlab
ANN toolbox works on feed forward back propagation algorithm is
used for modeling purpose. 3-12-1 network structure having
minimum average prediction error found as best network architecture
for predicting surface roughness value. The network predicts surface
roughness for unseen data and found that the result/prediction is
better. For desired surface finish of the component to be produced
there are many different combination of cutting parameters are
available. The optimum cutting parameter for obtaining desired
surface finish, to maximize tool life is predicted. The methodology is
demonstrated, number of problems are solved and algorithm is coded
in MatlabĀ®.", keywords = "End milling, Surface roughness, Neural networks.", volume = "5", number = "9", pages = "1781-7", }