Modeling & Analysis of End Milling Process Parameters Using Artificial Neural Networks

Abstract:

For modeling and optimizing the process parameters of manufacturing problems in the present days, numerical and Artificial Neural Networks (ANN) methods are widely using. In manufacturing environments, main focus is given to the finding of Optimum machining parameters. Therefore the present research is aimed at finding the optimal process parameters for End milling process. The End milling process is a widely used machining process because it is used for the rough and finish machining of many features such as slots, pockets, peripheries and faces of components. The present work involves the estimation of optimal values of the process variables like, speed, feed and depth of cut, whereas the metal removal rate (MRR) and tool wear resistance were taken as the output .Experimental design is planned using DOE. Optimum machining parameters for End milling process were found out using ANN and compared to the experimental results. The obtained results provβed the ability of ANN method for End milling process modeling and optimization.

• Periodical: Applied Mechanics and Materials (Volumes 592-594)
• Pages: 2733-2737
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.592-594.2733
• Online since: July 2014
• Authors: G. Harinath Gowd*, K. Divya Theja, Peyyala Rayudu, M. Venugopal Goud, M .Subba Roa
• Keywords: AISI S2, Central Composite Design, End-Mill Process

References

[1] Mike S. Lou., Joseph C. Chen., and Caleb M. Li., 1999. Surface roughness prediction technique for CNC end milling. Journal of Industrial Technology, Vol. 17, No. 2-7, pp.2-6.

[2] Optimization of Cutting Conditions in End Milling Process with the Approach of Particle Swarm Optimization Vikas Pare, Geeta Agnihotri & C.M. Krishna Mechanical Engineering Department, Maulana Azad National Institute of Technology, Bhopal, India.

DOI: 10.47893/ijmie.2011.1025

[3] Oktem and Erzurumlu 2006 Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm, Vol 27, pp.735-744.

DOI: 10.1016/j.matdes.2005.01.010

[4] Haron and Shariff (2010) Application of GA to optimize cutting condition for minimizing surface roughness in end milling machining process, Vol37, pp.4650-4659 I. N.

DOI: 10.1016/j.eswa.2009.12.043

[5] Tansela W.Y. Baoa, N.S. Reena, C.V. Kropas-Hughesb , Genetic tool monitor (GTM)for micro-end-milling operations Oct2004. International Journal of machine tools &manufacture , volume 45, issue 3.

DOI: 10.1016/j.ijmachtools.2004.08.013