Advance in Monitoring and Process Control of Surface Roughness

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.





References:
<p>[1] Somkiat T. (2011). &ldquo;In-Process Monitoring and Prediction of Surface
Roughness in CNC Turning Process&rdquo;. Advance Materials Research 199-
200. pp. 1958-1966.
[2] Somkiat T. and Augsumalin S. (2012). &ldquo;Intelligent Monitoring and
Prediction of Surface Roughness in Ball-End Milling Process&rdquo;. Applied
Mechanics and Materials Vols. 121-126. pp. 2059-2063.
[3] Somkiat T. and Voraman B. &ldquo;Integration of In-Process Monitoring and
Statistical Process Control of Surface Roughness on CNC Turning
Process.&rdquo; Journal of Computer Integrated Manufacturing.
[4] Cakir M. C., C. Ensarioglu, and I. Demirayak (2009). &ldquo;Mathematical
models of surface roughness for evaluating the effects cutting
parameters and coating materials.&rdquo; Journal of materials processing
technology, Vol. 209, pp. 102-109.
[5] Choudhury S.H. and G. Bartarya. (2003). &ldquo;Role of temperature and
surface finish in predicting tool wear using neural network and design of
experiments.&rdquo; International Journal of Machine Tools &amp; Manufacture,
Vol. 43, pp. 747-753.
[6] Davim J. P., V.N. Gaitonde, and S.R. Karnik (2008). &ldquo;Investigations into
the effect of cutting conditions on surface roughness in turning of free
machining steel by ANN models.&rdquo; Journal of materials processing
technology, Vol. 205, pp. 16-23.
[7] Feng C.X. and X.F. Wang. (2003). &ldquo;Surface roughness predictive
modeling: neural networks versus regression.&rdquo; IIE Transactions, Vol.
35-1, pp. 11-27.
[8] Ignatov M.G., A.E. Perminov, and E. Yu. Prokof&rsquo;ev. (2008). &ldquo;Influence
of the vertical cutting force on the surface precision and roughness in
opposed milling.&rdquo; Russian Engineering Research, Vol. 28-9, pp. 864-
865.
[9] Lalwani D.I., N.K. Mehta., and P.K. Jain. (2008). &ldquo;Experimental
investigation of cutting parameters influence on cutting forces and
surface roughness in finish hard turning of MDN250 steel.&rdquo; Journal of
materials processing technology, Vol. 206, pp. 167-179.
[10] Lu C. (2008). &ldquo;Study on prediction of surface quality in machining
process.&rdquo; Journal of materials processing technology, Vol. 205, pp. 439-
450.
[11] Lee J.H., D.E. Kim, and S.J. Lee. (1998). &ldquo;Statistical analysis of cutting
force ratios for flank-wear monitoring.&rdquo; Journal of Materials Processing
Technology, Vol. 74, pp. 104-114.
[12] Moriwaki T., T. Shibasaka, and T. Somkiat. (2004). &ldquo;Development of
in-process tool wear monitoring system for CNC turning.&rdquo; International
Journal of Japan Society of Mechanical Engineers, Series C, Vol. 47-3,
pp. 933-938.
[13] Tlusty J. and G.C. Andrews. (1983). &ldquo;A critical review of sensors for
unmanned machining.&rdquo; CIRP Annals, pp. 563-572.</p>