Multi-Objective Optimization of Milling Parameters for Machining Cast Iron on Machining Centre

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Multi-Objective Optimization of Milling Parameters for Machining Cast Iron on Machining Centre

Author Affiliations

¹ R.V.R and J.C College Of Engineering

Res. J. Engineering Sci., Volume 2, Issue (5), Pages 35-39, May,26 (2013)

Abstract

This paper presents an approach for determination of the best cutting parameters leading to minimum surface roughness and maximum Material Removal Rate in machining Cast Iron on Machining Centre. A feed forward neural network model is developed exploiting experimental values. The neural network model is trained and tested in MATLAB. Multi objective Genetic algorithm coupled with neural network is employed to find optimum cutting parameters leading to minimum surface roughness and maximum Material Removal Rate.

References

Hasan Oktem and Tuncay Erzurumlu, Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm, Science Direct Materials and Design, 735-744 (2006)
Ghani J.A. and I.A. Choudhury Application of Taguchi method in the optimization of end milling parameters, Journal of Materials Processing Technology 84–92 (2004)
Benardos P.G. and Vosniakos G.C., Prediction of surface roughness in CNC face milling using neuralnetworks and Taguchi’s design of experiments, Robotics and Computer-Integrated Manufacturing,18(5-6), 343-354 (2002)
Sanjitmoshat, Saurav data, Ashishbandopaddhayay, pradipkumar pal Optimization ofCNC milling process parameters using PCA based Taguchi method. International journal of Engineering Science and technology,2(1), 92-102 (2010)
Azlan Mohd Zain and Habibollah Haron, Genetic Algorithm for Optimizing Cutting Conditions of Uncoated Carbide (WC-Co) in Milling Machining Operation, Monash University, Sunway campus, Malaysia. IEEE (2009)
Sharma V.S., Dhiman S., Sehgal R. and Sharma S.K., Estimation of cutting forces and surfaceroughness for hard turning using neural networks, Journal of Intelligent Manufacturing, 19(4), 473-483 (2008)
Karayel D., Prediction and control of surfaceroughness in CNC lathe using artificial neuralnetwork, Journal of Materials Processing Technology, 209(7),3125-3137(2009)
Soleymaniyazdi M.R. and Khorram A., Modeling and Optimization of Milling Process by using RSM and ANN Methods, IACSIT International Journal of Engineering and Technology, 2(5),(2010)
JaliliSaffar R. and Razfar M.R., Optimization of Machining Parameters to Minimize Tool Deflection in the End Milling Operation Using Genetic Algorithm, World Applied Sciences Journal 6(1) 64-69 (2009)
Wang Z.H. and Yuan J.T., Surface Roughness Prediction and Cutting Parameters Optimization in High-Speed Milling A Mn1Cu Using Regression and Genetic Algorithm, International Conference on Measuring Technology and Mechatronics Automation 978-0-7695-3583-8/09 IEEE (2009)
Bouzakis K.D. and Paraskevopoulou R., Multi-Objective Optimization of Cutting Conditions In Milling Using Genetic Algorithms, International Conference on Manufacturing Engineering (ICMEN) (2008)

[ref1] Hasan Oktem and Tuncay Erzurumlu, Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm, Science Direct Materials and Design, 735-744 (2006)

[ref2] Ghani J.A. and I.A. Choudhury Application of Taguchi method in the optimization of end milling parameters, Journal of Materials Processing Technology 84–92 (2004)

[ref3] Benardos P.G. and Vosniakos G.C., Prediction of surface roughness in CNC face milling using neuralnetworks and Taguchi’s design of experiments, Robotics and Computer-Integrated Manufacturing,18(5-6), 343-354 (2002)

[ref4] Sanjitmoshat, Saurav data, Ashishbandopaddhayay, pradipkumar pal Optimization ofCNC milling process parameters using PCA based Taguchi method. International journal of Engineering Science and technology,2(1), 92-102 (2010)

[ref5] Azlan Mohd Zain and Habibollah Haron, Genetic Algorithm for Optimizing Cutting Conditions of Uncoated Carbide (WC-Co) in Milling Machining Operation, Monash University, Sunway campus, Malaysia. IEEE (2009)

[ref6] Sharma V.S., Dhiman S., Sehgal R. and Sharma S.K., Estimation of cutting forces and surfaceroughness for hard turning using neural networks, Journal of Intelligent Manufacturing, 19(4), 473-483 (2008)

[ref7] Karayel D., Prediction and control of surfaceroughness in CNC lathe using artificial neuralnetwork, Journal of Materials Processing Technology, 209(7),3125-3137(2009)

[ref8] Soleymaniyazdi M.R. and Khorram A., Modeling and Optimization of Milling Process by using RSM and ANN Methods, IACSIT International Journal of Engineering and Technology, 2(5),(2010)

[ref9] JaliliSaffar R. and Razfar M.R., Optimization of Machining Parameters to Minimize Tool Deflection in the End Milling Operation Using Genetic Algorithm, World Applied Sciences Journal 6(1) 64-69 (2009)

[ref10] Wang Z.H. and Yuan J.T., Surface Roughness Prediction and Cutting Parameters Optimization in High-Speed Milling A Mn1Cu Using Regression and Genetic Algorithm, International Conference on Measuring Technology and Mechatronics Automation 978-0-7695-3583-8/09 IEEE (2009)

[ref11] Bouzakis K.D. and Paraskevopoulou R., Multi-Objective Optimization of Cutting Conditions In Milling Using Genetic Algorithms, International Conference on Manufacturing Engineering (ICMEN) (2008)