Optimized Machining Condition Selection for High-Quality Surface in High-Speed Finish Milling of Molds


Article Preview

Today, the trend in die and mold manufacturing is to pursue high-quality surface topology using high-speed finish milling operation. This paper presents a new approach to optimize machining conditions according to the required material removal rate (MRR), focusing on obtaining a high-quality surface. In this approach, the prediction model of surface roughness using the 2-staged artificial neural network (ANN) is employed for the objective function. Furthermore, an additional surface quality criterion is also used for the optimization problem using the genetic algorithm. It has been investigated that optimized machining conditions can be selected to obtain the high-quality surface within allowable reliability while maintaining a high-quality surface, under the given desired MRR.



Edited by:

Dongming Guo, Tsunemoto Kuriyagawa, Jun Wang and Jun’ichi Tamaki




S. W. Lee et al., "Optimized Machining Condition Selection for High-Quality Surface in High-Speed Finish Milling of Molds", Key Engineering Materials, Vol. 329, pp. 711-718, 2007

Online since:

January 2007




[1] S.V. Wong, A.M.S. Hamouda and M.A. El Baradie: Generalized fuzzy model for metal cutting data selection, J. of Materials Processing Technology, Vol. 89-90 (1999), pp.310-317.

DOI: https://doi.org/10.1016/s0924-0136(99)00127-2

[2] U. Zuperl, F. Cus, B. Mursec and T. Ploj: A hybrid analytical-neural network approach to the determination of optimal cutting conditions, J. of Materials Processing Technology, Vol. 157-158 (2004), pp.82-90.

DOI: https://doi.org/10.1016/j.jmatprotec.2004.09.019

[3] L. Wang: A hybrid genetic algorithm-neural network strategy for simulation optimization, Applied Mathematics and Computation, Vol. 170 (2005), Issue 2, pp.1329-1343.

DOI: https://doi.org/10.1016/j.amc.2005.01.024

[4] J. Vivancos, C.J. Luis, L. Costa and J.A. Ortiz: Optimal machining parameters selection in high speed milling of hardened steels for injection moulds, J. of Materials Processing Technology, Vol. 155-156 (2004), pp.1505-1512.

DOI: https://doi.org/10.1016/j.jmatprotec.2004.04.260

[5] J. -S. Chen, Y. -K. Huang and M. -S. Chen: A study of the surface scallop generating mechanism in the ball-end milling process, Int. J. of Machine Tools & Manufacture, Vol. 45 (2005), pp.1077-1084.

DOI: https://doi.org/10.1016/j.ijmachtools.2004.11.019

[6] P.G. Genardos and G.C. Vosniakos: Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments, Robotics and Computer Integrated Manufacturing, Vol. 18 (2002), pp.343-354.

DOI: https://doi.org/10.1016/s0736-5845(02)00005-4

[7] Y. Jiao, S. Lei, Z.J. Pei and E.S. Lee: Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations, Int. J. of Machine Tool & Manufacture, in print, (2004).

DOI: https://doi.org/10.1016/j.ijmachtools.2004.06.004

[8] S. -W. Lee, J. -Y. Won, S. -H. Nam, W. -P. Hong and H. -Z. Choi: Construction of high-speed machining DB and Prediction of Machining Characteristics for Web-Based Distributed Machining Systems, Journal of Industrial Technology, Vol. 11 (2004).