Intelligent Monitoring and Prediction of Surface Roughness in Ball-End Milling Process

Abstract:

In order to realize the intelligent machines, the practical model is proposed to predict the in-process surface roughness during the ball-end milling process by utilizing the cutting force ratio. The ratio of cutting force is proposed to be generalized and non-scaled to estimate the surface roughness regardless of the cutting conditions. The proposed in-process surface roughness model is developed based on the experimentally obtained data by employing the exponential function with five factors of the spindle speed, the feed rate, the tool diameter, the depth of cut, and the cutting force ratio. The prediction accuracy and the prediction interval of the in-process surface roughness model at 95% confident level are calculated and proposed to predict the distribution of individually predicted points in which the in-process predicted surface roughness will fall. All those parameters have their own characteristics to the arithmetic surface roughness and the surface roughness. It is proved by the cutting tests that the proposed and developed in-process surface roughness model can be used to predict the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy.

• Periodical: Applied Mechanics and Materials (Volumes 121-126)
• Pages: 2059-2063
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.121-126.2059
• Online since: October 2011
• Authors: Somkiat Tangjitsitcharoen, Angsumalin Senjuntichai
• Keywords: Ball-End Milling Process, Cutting Force Ratio, Monitoring, Prediction, Surface Roughness (SR)

References

[1] C.M. Cakir, C. Ensarioglu and I. Demirayak: Journal of Materials Processing Technology, Vol. 209 (2009), pp.102-109.

[2] P.G. Benardos and G.C. Vosniakos: International Journal of Machine Tools and Manufacture Vol. 43 (2003), pp.833-844.

DOI: 10.1016/s0890-6955(03)00059-2

[3] M. Fontaine, A. Moufki, A. Devillez and D. Dudzinski: International Journal of Machine Tools and Manufacturing, Vol. 189 (2006) , pp.367-380.

[4] B. Ozcelik and M. Bayramoglu: International Journal of Machine Tools and Manufacturing, Vol. 46 (2006), pp.1395-1402.

[5] C.X. Feng, and X.F. Wang: IIE Transactions, Vol. 35 (2003), pp.11-27.

[6] T. Moriwaki, T. Shibasaka and T. Somkiat: International Journal of Japan Society of Mechanical Engineers, Series C, Vol. 47 (2004), pp.933-938.

[7] A.M. El-Tamimi and T.M. El-Hossainy: Material and Manufacturing Processes, Vol. 23 (2008), pp.427-438.

[8] M.S. Lou, J.C. Chen and C.M. Li: Journal of Industrial Technology, Vol. 151 (1999), pp.1-6.

[9] J.H. Lee, D.E. Kim and S. J Lee: Journal of Materials Processing Technology, Vol. 74 (1989), pp.104-114.

[10] T. Somkait: NAMRI/SME, Vol. 38 (2010), pp.307-315.