Email this article 
Boring Tool Chatter Suppression Using Magneto-Rheological Fluid Damper through Regression Models
(*) Corresponding author
DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
Abstract
Chatter is a problem of instability in the metal cutting process, especially in boring operation when the length of the hole to be bored is high. The phenomenon is characterized by violent vibrations, loud noise and poor quality of surface finish. Chatter causes a reduction of the life of the tool and affects the productivity by interfering with the normal functioning of the machining process. Many academic and industrial engineers reported the chatter suppression in turning operation; however, for boring operation, there is a lot of scope for studying the chatter suppression problem. This paper presents the use of magneto-rheological fluid for active chatter suppression through regression models. The vibration signals corresponding to different machining conditions were captured. The relevant features of the signals were extracted. The characterized magneto-rheological fluid was taken and regression models were built to suppress the tool chatter based on on-line vibration measurement. The models built in the study are linear regression model, support vector regression model and artificial neural network model. The regression model results are validated.
Copyright © 2013 Praise Worthy Prize - All rights reserved.
Keywords
References
F. Kuster and P.E. Gygax, Cutting Dynamics and Stability of Boring Bars, CIRP Annals- Journal of Manufacturing Technology 39 (1990) 361-366.
R.A. Mahdavinejad, A step forward to chatter analysis in turning machines, 13th International Scientific conference on achievements in mechanical and material Engineering, 2005
Linus Andr´en, Lars H˚akansson, Anders Brandt and Ingvar Claesson, “Identification of Dynamic Properties of Boring Bar Vibrations in a Continuous Boring Operation”, Journal of Mechanical Systems & Signal Processing 18 (2004) 869-901.
Henrik Akesson, Tatiana Smirnova and Lars H˚akansson, Analysis of Dynamic Properties of Boring Bars Concerning Different Clamping Conditions, Journal of Mechanical Systems and signal processing 23 (2009) 2629- 2647.
Satoshi Ema , Etsuo Marui, Suppression of chatter vibration of boring tools using impact dampers, International Journal of Machine Tools & Manufacture 40 (2000) 1141–1156.
Li Chen Jung, Ulsoy A. Galip , Endres William J. The Effect of Flexible-Tool Rotation on Regenerative Instability in Machining, Journal of Manufacturing Science and Engineering 125(2003) 39-47.
Y. Liu, H.Matsuhisa, H. Utsuno, J.G.Park, Vibration isolation by a variable stiffness and damping system, International Journal of Japan Society of Mechanical Engineering (Series C) 48 (2) (2005) 305-310
Deqing Mei, Tianrong Kong, Albert J. Shih and Zichen Chen, Magnetorheological fluid-controlled boring bar for chatter suppression, Journal of material Processing Technology 209 (2009) 1861-1870.
Yanqing Liu, Hiroshi Matsuhisa and Hideo Utsuno, Semi- active vibration isolation system with variable stiffness and damping control, Journal of sound and vibration 313(2008) 16-28
Lonnie Houck III, Tony L. Schmitz and K. Scott Smith, Tunable holder for boring bars, Journal of Manufacturing Science and Engineering 127/47 81-790.
Amit A. Ghate, Seshu. P , Active vibration control of a smart beam, Proceedings of the SPIE 5062 (2003) 605-612.
Angela Trego, Paul F. Eastman, William F. Pratt, and C. G. Jensen, Reduced boring bar vibrations using damped composite structures, Proceedings of the 1995 Design Engineering Technical Conferences 3 (1995) 305-311.
Sanjiv G . Tewani, Keith E. Rouch and Bruce L. Walcott, A Study of cutting process stability of a boring bar with active dynamic absorber, International Journal of Machine Tools & Manufacture 35 (1995) 91–108
Linus Pettersson, Lars H˚akansson, Ingvar Claesson and Sven Olsson, “Active Control of Boring Bar Vibration in Cutting Operations”, International Conference on Sound and Vibration 9, Orlando, Florida, USA (2002).
A. Ganguli, A. Deraemaeker and A. Preumont, Regenerative chatter reduction by active damping control, Journal of sound and vibration 300 (2007) 847- 862.
C. Mei, Active regenerative chatter suppression during boring manufacturing process, Journal of Robotics and Computer integrated manufacturing 21 (2005) 153-158.
Salem, M.H., Anany, M.N., El-Habrouk, M., Rezeka, S.F., Performance of a dynamic vibration absorber using a magneto-rheological damper, (2012) International Review of Mechanical Engineering (IREME), 6 (6), pp. 1146-1156.
Katsuaki Sunakoda, Hiroshi Sodeyama, Norio Iwata, Hideo Fujitani and Satsuya Soda, Dynamic Characteristics of Magneto-Rheological Fluid Damper, Proc., SPIE’s 7th Annual Int. Symposium on Smart Structures and Materials, No.3989-20, 2000.
Srinivasan, S., Sathyanarayan, R., Srivatsan, L., Vijaya Ramnath, B., Optimization of tool life using linear regression analysis, (2012) International Review of Mechanical Engineering (IREME), 6 (3), pp. 405-410.
Pierpaolo D'Urso, Tommaso Gastaldi, A least-squares approach to fuzzy linear regression analysis, Computational Statistics & Data Analysis, 34, Issue 4, 28 October 2000, Pages 427-440, ISSN 0167-9473.
Gil Gonzalez-Rodriguez, Angela Blanco, Ana Colubi, M. Asuncion Lubiano, Estimation of a simple linear regression model for fuzzy random variables, Fuzzy Sets and Systems, Volume 160, Issue 3.
A. J. Smola and B. Scholkopf, A tutorial on support vector regression, NeuroCOLT Technical report NC-TR-98-030, 1998 (Royal Halloway College, University of London, UK).
V. Vapnik, The nature of statistical learning theory (Springer, New York, 1995).
V. Vapnik, Statistical learning theory, (John Wiley and Sons, New York, 1998).
Bradley, I., Introduction to Neural Networks (Multinet Systems Pvt Ltd 1997).
Fadalla, A., Lin, Chien-Hua. “An Analysis of the Applications of Neural Networks in Finance”, Interfaces 31: 4 July- August 2001 pp. 112-122.
Schwarzer, G., Vach, W., Schumacher, M., “On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology”, URL citeseer.nj.nec.com/44173.html.
Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten (2009); The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1.
Refbacks
- There are currently no refbacks.
Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize
