Effect Analysis and ANN Prediction of Surface Roughness in End Milling AISI H13 Steel

Abstract:

Surface roughness has a significant effect on the performance of machined components. In the present study, a total of 49 end milling experiments on AISI H13 steel are conducted. Based on the experimental results, the signal-to-noise (S/N) ratio is employed to study the effects of cutting parameters (axial depth of cut, cutting speed, feed per tooth and radial depth of cut) on surface roughness. An ANN predicting model for surface roughness versus cutting parameters is developed based on the experimental results. The testing results show that the proposed model can be used as a satisfactory prediction for surface roughness.

• Periodical: Materials Science Forum (Volumes 800-801)
• Pages: 590-595
• DOI: https://doi.org/10.4028/www.scientific.net/MSF.800-801.590
• Online since: July 2014
• Authors: Qing Zhang, Song Zhang*, Jia Man, Bin Zhao
• Keywords: Artificial Neural Network (ANN), End Milling, S/N Ratio, Surface Roughness (SR)

References

[1] Y.W. Wang, S. Zhang, J.F. Li, et al.: Adv. Mater. Res. Vol. 126 (2010), p.911.

[2] P.G. Benardos, G.C. Vosniakos: Int. J. Mach. Tool & Manu. Vol. 43 (2003), p.833.

[3] Z.W. Zhong, L.P. Khoo, S.T. Han: Int. J. Adv. Manu. Tech. Vol. 28 (2006), p.688.

[4] S. Zhang, J.F. Li: Adv. Mater. Res. Vol. 325 (2011), p.418.

[5] A.M. Zain, H. Haron, S. Sharif: Exp. Syst. Appl. Vol. 37 (2010), p.1755.

[6] S. Palani, U. Natarajan: Int. J. Adv. Manu. Tech. Vol. 54 (2011), p.1033.

[7] H. Öktem: Int. J. Adv. Manu. Tech., Vol. 43 (2009), p.852.

[8] İ. Asiltürk, M. Çunkaş: Exp. Syst. Appl. Vol. 38 (2011), p.5826.

[9] I.N. Tansel, B. Ozcelik, W.Y. Bao, et al.: Int. J. Mach. Tool & Manu. Vol. 46 (2006), p.26.

[10] J.C. Chen, J.C. Chen: Int. J. Adv. Manu. Tech. Vol. 25 (2005), p.427.