Evaluation of Surface Quality and Signal Characteristics in Milling Process of Al7075-T651

M.C. Hwang; Joon Young Koo; Yong Ki Choi; Hyun Jung Kim; Jeong Suk Kim

doi:10.4028/www.scientific.net/KEM.737.95

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HomeKey Engineering MaterialsKey Engineering Materials Vol. 737Evaluation of Surface Quality and Signal...

Evaluation of Surface Quality and Signal Characteristics in Milling Process of Al7075-T651

Abstract:

Recently, as the automotive and aerospace industry research has focused one weight lightening, the use of functional aluminum alloys has been increasing. Aluminum alloys are effective materials because of their high specific strength and high stiffness ratio. However, machining deformation and heat deflection can occur depending on the machining type. Owing to these difficult-to-cut characteristics, it is necessary to monitor the machined surface quality of aluminum alloys. In this paper, we study the correlation between surface quality, namely burr formation and surface roughness, related to cutting parameters and signals obtained from multiple sensors. The output signals are measured by an acoustic emission (AE) sensor and an accelerometer and are analyzed in the signal frequency domain. By using the wavelet transform of analyzed signals, we determine the correlation between surface quality and signals. Based on this investigation, a surface quality monitoring system can be suggested.

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Periodical:

Key Engineering Materials (Volume 737)

Pages:

95-100

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.737.95

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Online since:

June 2017

Authors:

M.C. Hwang, Joon Young Koo, Yong Ki Choi, Hyun Jung Kim, Jeong Suk Kim*

Keywords:

Al7075-T651, Aluminum Alloys, Milling Process, Monitoring, Signal Process, Surface Quality

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References

[1] G. L. Chern, Experimental observation and analysis of burr formation mechanisms in face milling of aluminum alloys, Int. J. Mach. Tools Manuf. 46(12-13) (2006) 1517-1525.

DOI: 10.1016/j.ijmachtools.2005.09.006

Google Scholar

[2] M. Y. Wang, H. Y. Chang, Experimental study of surface roughness in slot end milling Al2014-T6, Int. J. Mach. Tools Manuf. 44(1) (2004) 51-57.

DOI: 10.1016/j.ijmachtools.2003.08.011

Google Scholar

[3] D. Vakondios, P. Kyratsis, S. Yaldiz, A. Antoniadis, Influence of milling strategy on the surface roughness in ball end milling of the aluminum alloy Al7075-T6, Meas. 45(6) (2012) 1480-1488.

DOI: 10.1016/j.measurement.2012.03.001

Google Scholar

[4] M. Subramanian, M. Sakthivel, R. Sudhakaran, Modeling and analysis of surface roughness of Al7075-T6 in end milling process using response surface methodology, Arabian J. Sci. Eng. 39(10) (2014) 7299-7313.

DOI: 10.1007/s13369-014-1219-z

Google Scholar

[5] M. Chen, B. Rong, G. Liu, Research on burr formation in milling Al-alloy, Adv. Mater. Res. 135 (2010) 164-169.

DOI: 10.4028/www.scientific.net/amr.135.164

Google Scholar

[6] R. X. Gao, R. Yan, Wavelet-theory and application for manufacturing, Springer, 2011, pp.17-48.

Google Scholar

[7] M. J. Gomez, C. Castejon, J. C. Garcia-Prada, Incipient Fault Detection in Bearings Through the use of WPT Energy and Neural Networks, in: G. Dalpiaz et al. (Eds. ), Advances in Condition Monitoring of Machinery in Non-Stationary Operations Part of the series Lecture Notes in Mechanical Engineering, Springer, 2014, pp.63-72.

DOI: 10.1007/978-3-642-39348-8_4

Google Scholar