Influence of Cutting Parameters on the Surface Quality of Round Parts Made from AZ61 Magnesium Alloy and Machined by Turning

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The use of magnesium alloys in construction of different components of the mechanical systems (such: cars, aerospace vehicles, medical equipment etc.) is very efficient not only because it leads to reduction of the systems weight but also because it leads to reduction or elimination of the environment polluting and to reduction of the energy consumption. Generally, the main factors that influence the quality of the machined surfaces are as follows: cutting parameters, material properties, geometry of the tools, cooling liquids and lubricants, physical and mechanical properties of the subsurface layers etc. Among the above mentioned factors, cutting parameters are the factors that strongly influence the quality of the machined surfaces. The present paper analysis the results of the experimental investigation performed to determine the influence of cutting parameters (cutting speed, feed rate and cutting depth) on the surface quality machined by turning the AZ61 magnesium alloy. The main characteristics of the machined surface quality analyzed in experimental investigation were the surface roughness and hardness. The main conclusions resulted from the results analysis were as follows: the decrease of the feed rate led to surface roughness decrease and hardness increase; the increase of the cutting speed also led to an improved surface quality.

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128-134

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November 2013

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] H. Chen, A.T. Alpas, Sliding wear map for the magnesium alloy Mg–9Al–0. 9Zn (AZ91), Wear 246: 106–116, (2000).

DOI: 10.1016/s0043-1648(00)00495-6

Google Scholar

[2] F. Froes,D. Eliezer, E. Aghion, Thescience, technology, and applications of magnesium, JOM Journal of the Minerals, Metals and Materials Society 50, 30–34, (1998).

DOI: 10.1007/s11837-998-0411-6

Google Scholar

[3] Xuhong Guo, Lijun Teng, Wei Wang, Tingting Chen, Study on the Cutting Properties about Magnesium Alloy when Dry Turning with Kentanium Cutting Tools, Advanced Materials Research Vols. 102-104 pp.653-657, (2010).

DOI: 10.4028/www.scientific.net/amr.102-104.653

Google Scholar

[4] F. Witte,V. Kaese, H. Haferkamp, E. Switzer, A. Meyer-Lindenberg, C.J. Wirth , H. Windhagen, In vivo corrosion of four magnesium alloys and the associated bone response, Biomaterials 26 3557–3563, (2005).

DOI: 10.1016/j.biomaterials.2004.09.049

Google Scholar

[5] M.P. Staiger, A.M. Pietak, J. Huadmai, G. Dias, Magnesium and its alloys as orthopedic biomaterials: a review, Biomaterials 27, 1728–1734, (2006).

DOI: 10.1016/j.biomaterials.2005.10.003

Google Scholar

[6] E. Gariboldi, Drilling a magnesium alloy using PVD coated twist drills, Journal of Materials Processing Technology 134, 287–295, (2003).

DOI: 10.1016/s0924-0136(02)01111-1

Google Scholar

[7] K. Weinert,I. Inasaki J.W. Sutherland, T. Wakabayashi, Dry machining and minimum quantity lubrication, CIRP Annals-Manufacturing Technology 53 511–537, (2004).

DOI: 10.1016/s0007-8506(07)60027-4

Google Scholar

[8] I. Polmear, Magnesium alloys and applications, Materials Science and Technology 101–16, (1994).

Google Scholar

[9] Minoru Arai, Sunao Sato, Makoto Ogawa and Hiroshi Shikata, Chip Control in Finish Cutting of Magnesium Alloy, Journal of Materials Processing Technology, Vol. 62 pp.341-344, (1996).

DOI: 10.1016/s0924-0136(96)02432-6

Google Scholar

[10] F.Z. Fang, L.C. Lee and X.D. Liu, Mean flank temperature measurement in high speed dry cutting of magnesium alloy, Journal of Materials Processing Technology, pp.119-123, (2005).

DOI: 10.1016/j.jmatprotec.2004.10.002

Google Scholar

[11] H.K. Tonshoff and J. Winkler, The influence of tool coatings in machining of magnesium, Surface and Coatings Technology, Vols. 94-95 pp.610-616, (1997).

DOI: 10.1016/s0257-8972(97)00505-7

Google Scholar

[12] Wang jiadi, Lu cheng, et al., Selection of Magnesium Die Casting Alloys Based on Neural Network, Modern Manufacturing Engineering, pp.36-38, (2002).

Google Scholar