White and Dark Layer Analysis Using Response Surface Methodology

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Abstract:

This paper presents a study of the influence of cutting conditions on the finished surface obtained after an hard turning process, in particular a case study is presented where AISI 52100 bearing steel is machined under different cutting conditions. An analysis carried out using Surface Response Methodology has been developed in order to study the influence of the main cutting parameters such as cutting speed, feed rate and workpiece initial hardness on white (WL) and dark layer (DL) thickness. The whole experimental campaign has been performed using a chamfered PCBN tool inserts without any cutting fluid. Results show an evident influence of cutting speed and feed rate on both white and dark layer thickness while less relevant is the contribute given from the workpiece hardness on defining WL and DL depth. Finally, a model to find the optimal process conditions to minimize white and dark layer thickness is developed.

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Key Engineering Materials (Volumes 504-506)

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1335-1340

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February 2012

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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[1] Y. Huang, Y.K. Chou, S.Y. Liang, CBN tool wear in hard turning: survey on research progresses, Int. J. Adv. Manuf. Technol. 35 2007 443–453.

DOI: 10.1007/s00170-006-0737-6

Google Scholar

[2] D. Singh, P.V. Rao, A surface rougness model for hard turning process, Int. J. Adv. Manuf. Technol. 32 2007 1115–1124.

DOI: 10.1007/s00170-006-0429-2

Google Scholar

[3] J.G. Lima, R.F. Ávila, A.M. Abrão, M. Faustino, J.P., Davim Hard turning: AISI 4340 high strength low alloy steel and AISI D2 cold work tool steel, J. Mat. Process. Technol. 169(3) 2005 388–395

DOI: 10.1016/j.jmatprotec.2005.04.082

Google Scholar

[4] A.E. Diniz, J.R. Ferreira, F.T. Filho, Influence of refrigeration/lubrication condition on SAE 52100 hardened steel turning at several cutting speeds, Int. J. Mach. Tools Manuf. 43(3) 2003 317–326

DOI: 10.1016/S0890-6955(02)00186-4

Google Scholar

[5] A. Iqbal, H. Ning, I. Khan, L. Liang, N.U. Dar, Modeling the effects of cutting parameters in MQL-employed finish hardmilling process using D-optimal method, J. Mater. Process. Technol. 199(1–3) 2008 379–390

DOI: 10.1016/j.jmatprotec.2007.08.029

Google Scholar

[6] J.M. Zhou, M. Andersson, J.E, Stahl The monitoring of flank wear on the CBN tool in the hard turning process, Int. J. Adv. Manuf. Technol. 22 2003 697–702

DOI: 10.1007/s00170-003-1569-2

Google Scholar

[7] R. Quiza, L. Figueira, J.P. Davim, Comparing statistical models and artificial neural networks on predicting the tool wear in hard machining D2 AISI steel, Int. J. Adv. Manuf. Technol. 37 2008 641–648

DOI: 10.1007/s00170-007-0999-7

Google Scholar

[8] A. Ramesh, S.N. Melkote, Analysis of white layers formed in hard turning of AISI 52100 steel, Mater. Sci. Eng. A 390 2005 88–97.

DOI: 10.1016/j.msea.2004.08.052

Google Scholar

[9] D.C. Montgomery, Design and Analysis of Experiments, 4th ed., Wiley, New York, 1997.

Google Scholar

[10] R.M. Meyers and D.C. Montfgomery, Response Surface Methodology, 2nd Edition, Wiley, New York, 2001.

Google Scholar

[11] G. E. P. Box and N. R. Draper, Empirical Model Building and Response Surfaces, John Wiley & Sons, New York, NY, USA, 1991.

Google Scholar