The Impact of Cutting Conditions on Cutting Forces Andchatter Length for Steels and Aluminium

Authors

  • Rama Kotaiah K K.L.College of Engineering, Vaddeswaram, Guntur (Dist), Andhra Pradesh-522502. 3 NUY University, South Korea.
  • Babu J K K.L.College of Engineering, Vaddeswaram, Guntur (Dist), Andhra Pradesh-522502.
  • srinivas J NUY University, South Korea.
  • Kolla srinivas RVR & JC College of Engineering , Chowdavaram, Guntur(Dist), Andhra Pradesh.

Keywords:

Chatter Length, Tool Overhanging Length, Cutting Parameters

Abstract

In the present study, an attempt has been made to investigate the effect of cutting parameters (cutting speed, feed rate and depth of cut) on cutting forces and chatter starting point length in finish turning of EN8 steel, EN24 Steel, Mild steel and Aluminium. Machining test cuts were conducted using sharp tool and the effects of cutting conditions (depth of cut, cutting speed and feed rate), tool overhanging length and work piece over hanging length studied. Here experiments were conducted on EN8 steel, EN24 Steel, Mild steel and Aluminium   at different cutting parameters and different overhanging lengths. Here chatter starting point length is measured from the free edge of the work piece and graphs were plotted between over hanging length verses cutting forces and chatter starting point length.

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References

G. Boothroyd ,1975 “ Fundamentals of Metal Machining and Machine Tools,1st ed, Scrapta Book Company”.

D.E. Dimla Sr., 1998 “ Multivariate tool condition monitoring in a metal cutting operation using ANNs, PhD Thesis” School of Engineering university of Wolverhampton.

P.M. Lister , 1993 “On-Line measurement of tool wear, PhD Thesis , department of Mechanical Engineering, UMIST, Manchester.

L. I. Burke, 1989 , “Automated identification of tool wear states in machining processes: an application of self-organising neural networks ,PhD Thesis, Department of Industrial Engineering and Operations Research, university of California at Berkeley,USA.

L.C. Lee,K.S. Lee, C.S. Gan,1989 “On the correlation between dynamic cutting force and tool wear, Int. J. Match. Tools Manuf. 29 (3) 259-303.

T.I. Liu, E.J. Ko, On-line recognition of drill wear via artificial neural networks, in: ASME’s Winter Annual Meeting, Monitoring and Control for Manufacturing Processes, 44, PED, 1990, pp. 101–110.

A. Noori-Khajavi, R. Komanduri, Frequency and time domain analyses of sensor signals in drilling—II. Investi-gation on some problems associated with sensor integration, Int. J. Mach. Tool Manufact. 35 (6) (1995) 795–815.

S.V.T. Elanayar, Y.C. Shin, S. Kumara, Machining condition monitoring for automation using neural networks, in: ASME’s Winter Annual Meeting, Monitoring and Control of Manufacturing Processes, 44, PED, 1990, pp. 85–100.

[59 D. Yan, T.I. El-Wardany, M.A.A. Elbestawi, Multi-sensor strategy for tool failure detection in milling, Int. J. Mach. Tools Manufact. 35 (3) (1995) 383–398.

E. Govekar, I. Grabec, Self-organising neural network application to drill wear classification, Trans. ASME J. Eng. Ind. 116 (1994) 233–238.

D.E. Dimla Sr., Application of perceptron neural networks to tool-state classification in a metal-turning operation, Int. J. Eng. Appl. AI. 12 (4) (1999) 471–477.

D.E. Dimla Jr., P.M. Lister, N. Leighton, A multi-sensor integration method of sensor signals in a metal cutting operation via the application of MLP neural networks, in: Proceedings of the Fifth IEE International Conference on Artificial Neural Networks, Cambridge, 1997, pp. 306–311.

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Published

2008-09-01

How to Cite

[1]
“The Impact of Cutting Conditions on Cutting Forces Andchatter Length for Steels and Aluminium”, JME, vol. 3, no. 3, pp. 178–184, Sep. 2008, Accessed: Nov. 22, 2024. [Online]. Available: https://smenec.org/index.php/1/article/view/640

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