Prediction of Surface Roughness by Hybrid Artificial Neural Network and Evolutionary Algorithms in End Milling

Document Type: Research Paper

Authors

1 Department of Industrial Engineering, Mazandaran University of Science and Technology, Mazandaran, Iran

2 Mazandaran University of Science and Technology, Mazandaran, Iran

3 Mechanical Engineering Department, Tehran University, Tehran, Iran

Abstract

Machining processes such as end milling are the main steps of production which have major effect on the quality and cost of products. Surface roughness is one of the considerable factors that production managers tend to implement in their decisions. In this study, an artificial neural network is proposed to minimize the surface roughness by tuning the conditions of machining process such as cutting speed, feed rate and depth of cut. The proposed network is tested by many test problems of Ghani et al.[1] study and the weights of network are optimized by using three meta-heuristics, genetic algorithm (GA), imperialist competitive algorithm (ICA). The results show the efficiency and accuracy of the proposed network.

Keywords

Main Subjects


[1] Ghani, J.A., Choudhury, I.A., and Hassan, H.H., “Application of Taguchi Method in the Optimization of End Milling Parameters”, Journal of Materials Processing Technology, Vol. 145, pp. 84–92, (2004).

 

[2] Shiuh-Tarng, C., Ding-I, L., An-Chen, L., and Wei-Hua, C., “Adaptive Control Optimization in End Milling using Neural Networks”, Int. J. Mach. Tools Manufact, Vol. 34, pp. 637-660, (1995).

 

[3] Suk-Hwan, S., and Yang-Soo, S., “Neural Network Modeling for Tool Path Planning of the Rough Cut in Complex Pocket Milling”,Journal of Manufacturing Systems, Vol. 15, pp. 295-304, (1996).

 

[4] Benardos, P.G., and Vosniakos, G.C., “Prediction of Surface Roughness in CNC Face Milling using Neural Networks and Taguchi’s Design of Experiments”, Robotics and Computer Integrated Manufacturing, Vol. 18, pp. 343–354, (2002).

 

[5] Hasan, O., Tuncay, E., and Fehmi, E., “Prediction of Minimum Surface Roughness in End Milling Mold Parts using Neural Network and Genetic Algorithm”, Materials and Design, Vol. 27, pp. 735–744, (2006). 

 

[6] Tandon, V., El-Mounayri, H., and Kishawy, H., “NC End Milling Optimization using Evolutionary Computation”, International Journal of Machine Tools & Manufacture, Vol. 42, pp. 595–605, (2002).

 

[7] Zuperl, U., Cus, F., and  Reibenschuh, M., “Neural Control Strategy of Constant Cutting Force System in End Milling”, Robotics and Computer-Integrated Manufacturing, Vol. 27, pp. 485–493, (2011).

 

[8] Song-Tae, S., Ko-Tae, J., and Young-Moon, L.,” Cutting Force Signal Pattern Recognition using Hybrid Neural Network in End Milling”, Trans. Nonferrous Met. Soc. China, Vol. 19, pp. 209−214, (2009).

 

[9] Lebaal, N., Nouari, M., and Ginting, A., “A New Optimization Approach Based on Kriging Interpolation and Sequential Quadratic Programming Algorithm for End Milling Refractory Titanium Alloys”, Applied Soft Computing, Vol. 11, pp. 5110–5119, (2011).

 

[10] Wen-Hsien, H., Jinn-Tsong, T., Bor-Tsuen, L., and Jyh-Horng, C., “Adaptive Network-based Fuzzy Inference System for Prediction of Surface Roughness in End Milling Process using Hybrid Taguchi-genetic Learning Algorithm”, Expert Systems with Applications, Vol. 36, pp. 3216–3222, (2009).

 

[11] Ey ¨up Sabr, T.,” The Role of Stepover Ratio in Prediction of Surface Roughness in Flat End Milling”, International Journal of Mechanical Sciences, Vol. 51, pp. 782–789, (2009).  

 

[12] Dimitrios, V., Panagiotis, K., Suleyman, Y., and Aristomenis, A., “Influence of Milling Strategy on the Surface Roughness in Ball End Milling of the Aluminum Alloy Al7075-T6”, Measurement, Vol. 45, pp. 1480–1488, (2012). 

 

[13] Atashpaz-Gargari, E., and Lucas, C., "Imperialist Competitive Algorithm: an Algorithm for Optimization Inspired by Imperialist Competition", Proceedings of the IEEE Congress on Evolutionary Computation, Singapore, pp. 4661–4667, (2007).