Prediction of Optimum Cutting Parameters to obtain Desired Surface in Finish Pass end Milling of Aluminium Alloy with Carbide Tool using Artificial Neural Network

Authors: Anjan Kumar Kakati, M. Chandrasekaran, Amitava Mandal, Amit Kumar Singh

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 Matlab®.

Keywords: End milling, Surface roughness, Neural networks.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1063264

References:

[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.