Advance in Monitoring and Process Control of Surface Roughness

Authors: Somkiat Tangjitsitcharoen, Siripong Damrongthaveesak

Abstract:

This paper presents an advance in monitoring and process control of surface roughness in CNC machine for the turning and milling processes. An integration of the in-process monitoring and process control of the surface roughness is proposed and developed during the machining process by using the cutting force ratio. The previously developed surface roughness models for turning and milling processes of the author are adopted to predict the inprocess surface roughness, which consist of the cutting speed, the feed rate, the tool nose radius, the depth of cut, the rake angle, and the cutting force ratio. The cutting force ratios obtained from the turning and the milling are utilized to estimate the in-process surface roughness. The dynamometers are installed on the tool turret of CNC turning machine and the table of 5-axis machining center to monitor the cutting forces. The in-process control of the surface roughness has been developed and proposed to control the predicted surface roughness. It has been proved by the cutting tests that the proposed integration system of the in-process monitoring and the process control can be used to check the surface roughness during the cutting by utilizing the cutting force ratio.

Keywords: Turning, milling, monitoring, surface roughness, cutting force ratio.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1087502

References:

[1] Somkiat T. (2011). “In-Process Monitoring and Prediction of Surface Roughness in CNC Turning Process”. Advance Materials Research 199- 200. pp. 1958-1966.
[2] Somkiat T. and Augsumalin S. (2012). “Intelligent Monitoring and Prediction of Surface Roughness in Ball-End Milling Process”. Applied Mechanics and Materials Vols. 121-126. pp. 2059-2063.
[3] Somkiat T. and Voraman B. “Integration of In-Process Monitoring and Statistical Process Control of Surface Roughness on CNC Turning Process.” Journal of Computer Integrated Manufacturing.
[4] Cakir M. C., C. Ensarioglu, and I. Demirayak (2009). “Mathematical models of surface roughness for evaluating the effects cutting parameters and coating materials.” Journal of materials processing technology, Vol. 209, pp. 102-109.
[5] Choudhury S.H. and G. Bartarya. (2003). “Role of temperature and surface finish in predicting tool wear using neural network and design of experiments.” International Journal of Machine Tools & Manufacture, Vol. 43, pp. 747-753.
[6] Davim J. P., V.N. Gaitonde, and S.R. Karnik (2008). “Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models.” Journal of materials processing technology, Vol. 205, pp. 16-23.
[7] Feng C.X. and X.F. Wang. (2003). “Surface roughness predictive modeling: neural networks versus regression.” IIE Transactions, Vol. 35-1, pp. 11-27.
[8] Ignatov M.G., A.E. Perminov, and E. Yu. Prokof’ev. (2008). “Influence of the vertical cutting force on the surface precision and roughness in opposed milling.” Russian Engineering Research, Vol. 28-9, pp. 864- 865.
[9] Lalwani D.I., N.K. Mehta., and P.K. Jain. (2008). “Experimental investigation of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel.” Journal of materials processing technology, Vol. 206, pp. 167-179.
[10] Lu C. (2008). “Study on prediction of surface quality in machining process.” Journal of materials processing technology, Vol. 205, pp. 439- 450.
[11] Lee J.H., D.E. Kim, and S.J. Lee. (1998). “Statistical analysis of cutting force ratios for flank-wear monitoring.” Journal of Materials Processing Technology, Vol. 74, pp. 104-114.
[12] Moriwaki T., T. Shibasaka, and T. Somkiat. (2004). “Development of in-process tool wear monitoring system for CNC turning.” International Journal of Japan Society of Mechanical Engineers, Series C, Vol. 47-3, pp. 933-938.
[13] Tlusty J. and G.C. Andrews. (1983). “A critical review of sensors for unmanned machining.” CIRP Annals, pp. 563-572.