A New Approach to Cutting Temperature Prediction Using Support Vector Regression and Ant Colony Optimization

Abstract:

In this paper, support vector regression with ant colony optimization is presented for the prediction of tool-chip interface temperature depends on cutting parameters in machining. Ant colony (ACO) optimization was developed to optimize three parameters of SVR, including penalty parameter C, insensitive loss function ε and kernel function σ. SVR constructs hyperplane in high dimension space and fits the data in non-linear form. Normalized mean square error (NMSE) of fitting result is used as target of ant colony optimization. ACO finds the best parameters which correspond to the NMSE. The results showed that the proposed approach, by comparing with back-propagation neural network model, was an efficient way to model tool–chip interface temperature with good predictive accuracy.

• Periodical: Advanced Engineering Forum (Volume 4)
• Pages: 145-152
• DOI: https://doi.org/10.4028/www.scientific.net/AEF.4.145
• Online since: June 2012
• Authors: De Huai Zeng, Yuan Liu, Lian Bo Jiang, Li Li, Gang Xu
• Keywords: Ant Colony Algorithm (ACA), Machining, Support Vector Regression (SVR), Temperature Prediction

References

[1] Lazoglu I, Altintas Y. Prediction of tool and chip temperature in continuous and interrupted machining. Int Jnl ofMach Tools Manu. 2002, 42(9): 1011-1022.

DOI: 10.1016/s0890-6955(02)00039-1

[2] Henriksen EK. Residual stresses in machined surfaces. Trans of ASME. 1951. 73(1): 69-76.

[3] H.G. Kim, J.H. Sim, and H.J. Kweon, Performance evaluation of chip breaker utilizing neural network, J Mater Process Technol, vol. 209, pp.647-656, Fed (2009).

DOI: 10.1016/j.jmatprotec.2008.02.064

[4] D. Karayel, Predition and control of surface roughness in CNC lathe using artificial neural network, J Mater Process Technol, vol. 209, pp.3125-3137, Fed (2009).

DOI: 10.1016/j.jmatprotec.2008.07.023

[5] S.S. Panda, D. Chakraborty, and S.K. Pal, Flank wear prediction in drilling using back propagation neural network and radial basis function network, Applied soft computing, vol. 8, Jul. 2008, pp.858-871.

DOI: 10.1016/j.asoc.2007.07.003

[6] Corinna Cortes, Vladimir Vapnik, Support-Vector Networks, Machine Learning, 20, pp.273-297 (1995).

DOI: 10.1007/bf00994018

[7] Pai, P.F., Lin, C.S., Hong, W.C., Chen, C.T.: A hybrid support vector machine regression for exchange rate prediction. International Journal of Information and Management Sciences 17, 19–32 (2006).

[8] Li Xuemei, Ding Lixing, Hybrid Genetic Algorithm and Support Vector Regression in Cooling Load Prediction, Proc. of 3rd Int. Conf. on Knowledge Discovery and Data Mining, 2010, pp.527-531.

DOI: 10.1109/wkdd.2010.136

[9] Li Xuemei, Shao Ming, et al. Particle Swarm Optimization-based LS-SVM for Building Cooling Load Prediction, Journal of Computers, Vol 5, No 4 (2010), 614-621.

DOI: 10.4304/jcp.5.4.614-621