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Performance Profiling of Nanoparticulate Graphite Powder as Lubricant in the Machining of AISI 1040 Steel under Variable Machining Conditions
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Acoustic Emission Signal Analysis for Tool Condition Monitoring in Microendmilling of Aluminium Alloy
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 984-985Acoustic Emission Signal Analysis for Tool...

Acoustic Emission Signal Analysis for Tool Condition Monitoring in Microendmilling of Aluminium Alloy

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Abstract:

In this study, the effect of tool wear is correlated with acoustic emission (AE) signal during microendmilling of aluminium alloy (AA 1100). The AE signals were acquired using Kistler make AE sensor and the signal features are analyzed in time domain (root mean square (RMS)) and frequency domain (dominant frequency and amplitude). The dominant frequency of the AE signal shows increasing trend with increase in the tool wear, where as AE_RMS show uneven trend. The discrete wavelet transformation technique (DWT) has also been carried out by decomposing the required AE signal in different frequency bands. The AE_RMS and specific AE energy were computed for the decomposed AE signals. From the specific AE energy, it is observed that shearing occurs during microendmilling and also found to be similar that of macro-regieme endmilling. The result demonstrated that the AE signals are potential indicator for tool condition monitoring in microendmilling.

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Periodical:

Advanced Materials Research (Volumes 984-985)

Pages:

25-30

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.984-985.25

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Online since:

July 2014

Authors:

Muniyandi Prakash, P. Ravisankar, Mani Kanthababu*

Keywords:

Acoustic Emission (AE), Discrete Wavelet Transformation, Microendmilling, Specific Ae Energy, Tool Wear

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* - Corresponding Author

[1] N.P. Mahalik, Micromanufacturing and Nanotechnology, Springer-Verlag, Berlin, Germany, (2006).

[2] V.K. Jain, Introduction to Micromachining, Narosa Publishing House Pvt. Ltd, New Delhi, India, (2010).

[3] D. Dornfeld, S. Min, and Y. Takeuchi, Recent advances in mechanical micromachining, Annals of CIRP, 55 (2006) 745-768.

DOI: 10.1016/j.cirp.2006.10.006

[4] A.B.M.A. Asad, T. Masaki, M. Rahman, H.S. Lim, and Y.S. Wong, Tool-based micro-machining, Journal of Material Processing Technology, 192-193 (2007) 204-211.

DOI: 10.1016/j.jmatprotec.2007.04.038

[5] M. Prakash and M. Kanthababu, In-process tool condition monitoring using acoustic emission sensor in microendmilling, International Journal for Machining Science and Technology, 17 (2013) 209-227.

DOI: 10.1080/10910344.2013.780541

[6] J. Chae, S.S. Park, and T. Freiheit, Investigation of micro-cutting operations, International Journal of Machine Tools and Manufacture, 20 (2005) 1-20.

[7] M. Kanthababu, M.S. Shunmugam, and M. Singaperumal, Multi-sensors based condition monitoring for honing of cylinder liners, International Journal of Manufacturing Research, 7 (2012) 376-396.

DOI: 10.1504/ijmr.2012.050102

[8] K. Jamielniak, Application of AE and cutting force signals in tool condition monitoring in micro-milling, Journal of Manufacturing Science and Technology, 1 (2008) 97-102.

DOI: 10.1016/j.cirpj.2008.09.007

[9] I. Inasaki, Application of acoustic emission sensor for monitoring machining processes, Ultrasonics, 36 (1998) 273-281.

DOI: 10.1016/s0041-624x(97)00052-8

[10] P. Souquet, N. Gsib, M. Deschamps, J. Roget, and J.C. Tanguy, Tool monitoring with acoustic emission-industrial results and future prospects, Annals of CIRP, 36 (1987) 57-60.

DOI: 10.1016/s0007-8506(07)62553-0

[11] X. Li, and Z. Yuan, Tool wear monitoring with wavelet packet transform-fuzzy clustering method, Wear, 219 (1998) 145-154.

DOI: 10.1016/s0043-1648(98)00165-3

[12] D.E. Lee, I. Hwang, C.M.O. Valente, J.F.G. Oliveira, and D.A. Dornfeld, Precision manufacturing process monitoring with acoustic emission, International Journal of Machine Tools and Manufacture, 46 (2006) 176-188.

DOI: 10.1016/j.ijmachtools.2005.04.001

[13] L. Dan, and J. Mathew, Tool wear and failure monitoring techniques for turning - a review, International Journal of Machine Tools and Manufacture, 30 (1990) 579-598.

DOI: 10.1016/0890-6955(90)90009-8

[14] S. Dolinsek, J. Kopac, Acoustic emission signals for tool wear identification, Wear, (1999) 225-229.

DOI: 10.1016/s0043-1648(98)00363-9

[15] X. Chen, B. Li, Acoustic emission method for tool condition monitoring based on wavelet analysis, International Journal of Advanced Manufacturing and Technology, 33 (2007) 968-976.

DOI: 10.1007/s00170-006-0523-5