Detection of the Development of Chatter in End Mil

Views:
 
     
 

Presentation Description

chatter detection

Comments

Presentation Transcript

Detection of the Development of Chatter in End Milling Operations by Using Index Based Reasoning (IBR) : 

Detection of the Development of Chatter in End Milling Operations by Using Index Based Reasoning (IBR) K. Bickraj Florida International University, Department of Mechanical and Materials Engineering, 10555 W. Flagler M.Demetgul Marmara University, Technical Education Faculty, Goztepe, Istanbul, Turkey I.N. Tansel Florida International University, Department of Mechanical and Materials Engineering, 10555 W. Flagler B. Kaya Department of Design and Manufacturing EngineeringGebze Institute of Technology, Gebze, Kocaeli, Turkey B. Ozcelik Department of Design and Manufacturing EngineeringGebze Institute of Technology, Gebze, Kocaeli, Turkey

Introduction : 

Introduction The basic (top) and general purpose (bottom) Index Based Reasoner (IBR)

Previous Studies : 

Previous Studies Analysis of Chatter Damage with S Transformation

Previous Studies : 

Previous Studies Inspection of Chatter Damage by Using Wavelet Transformations

Block diagram of a simulated IBR for chatter monitoring : 

Block diagram of a simulated IBR for chatter monitoring

Experimental Setup : 

Experimental Setup

Picture of Chatter : 

Picture of Chatter

Cutting torque time series containing excessive vibrations : 

Cutting torque time series containing excessive vibrations

Cutting force time series containing excessive vibrations : 

Cutting force time series containing excessive vibrations

Health index values that capture excessive chatter : 

Health index values that capture excessive chatter

CONCLUSION : 

CONCLUSION Use of IBR was proposed for detection of chatter in milling operations. The IBR simulator was established by using the MATLAB. The main advantage of the IBR is letting the engineers to develop the system by assigning numbers to different levels of the sensory signals. Objective is to program microcontrollers from this program. Once, the IBR is developed it may be emulated either using MATLAB or its own simulator. The feasibility of the proposed method was investigated on the data of end milling operation. The HI indicated chatter by raising the value of the HI when the vibrations reach to severe levels.

Slide 12: 

REFENCES [1] Tobias S.A., 1965, “Machine Tool Vibration,” Blackie and Son. [2] Koenigsberger F., Tlusty J., 1970, “Machine Tool Structures,” Pergamon Press, Oxford, England. [3] Tansel I. N., Li M., Yapici A., 2007, “Evaluation of Performance of the Index Based Reasoning (IBR) at a Simulated UAV,” Structural Health Monitoring 2007 (Proceedings of the 6th International Workshop on Structural Health Monitoring (IWSHM 2007)) Edited by Fu-Kuo Chang, DEStech Publications, pp. 264. [4] Sata T., Inamura T., 1975, “Development of Method to Predict and Prevent Chattering in Metal Cutting,” Annals of the CIRP, Vol. 24, pp. 309‑314. [5] Tsai S. Y., Eman K. F., Wu S. M., 1983, “Chatter Suppression in Turning,” Proceedings of NAMR Conference, pp. 399-402. [6] Clancy B.E., Shin Y.C., 2002, “A Comprehensive Chatter Prediction Model for Face Turning Operation Including the Tool Wear Effect,” International Journal of Machine Tools and Manufacture, Vol. 42(9), pp. 1035-1044. [7] Chiou R.Y., Richard Y., Steven Y., 2000, “Analysis of Acoustic Emission in Chatter Vibration with Tool Wear Effect in Turning,” International Journal of Machine Tools and Manufacture, Vol. 40(7), pp. 927-941. [8] Rao B.C., Shin Y.C., 1999, “A Comprehensive Dynamic Cutting Force Model for Chatter Prediction in Turning,” International Journal of Machine Tools and Manufacture, Vol. 39(10), pp. 1631-1654. [9] Gradisek J., Govekar E., Brabec I., 1998, “Time Series Analysis in Metal Cutting: Chatter Versus Chatter-Free Cutting,” Mechanical Systems and Signal Preprocessing, Vol. 12(6), pp. 839-854. [10] Chen S.G., Ulsoy A.G., Koren Y., 1996, “Computational Stability Analysis of Chatter in Turning,” Journal of Manufacturing Science and Engineering, Vol. 119, pp. 457-460. [11] El-Wardani T., Younis M.A., 1987, “Theoretical Analysis of Grinding Chatter,” Transaction of the ASME, Journal of Engineering for Industry, Vol. 109(4), pp. 314-320. [12] Devillez A., Dudzinski D., 2007, “ Tool vibration detection with eddy current sensors in machining process and computation of stability lobes using fuzzy classifiers,” Mechanical Systems and Signal Processing, Vol. 21(1), pp. 441-456. [13] Wang M., Fei R., 2001, “On-line chatter detection and control in boring based on an electrorheological fluid,” Mechatronics, Vol. 11(7), pp. 779-792. [14] Liang M., Xu D., 2006, “A Fuzzy Self-Learning Approach to Chatter Control in Milling,” Control and Applications. [15] Cho D. W., Eman K. F., 1988, “Pattern recognition for on-line chatter detection,” Mechanical Systems and Signal Processing, Vol. 2(3), pp. 279-290. [16] Pan G., Xu H., Kwan C.M., Liang C., Haynes L., Geng Z., 1996, “Modeling and Intelligent Chatter Control Strategies for a Lathe Machine,” IEEE Conference on Control Applications–Proceedings, Control Engineering Practice, Vol. 4(12), pp. 235-240. [17] Yang F., B. Zhang, Yu J., 1999, “Chatter Suppression Via an Oscillating Cutter,” Journal of Manufacturing Science and Engineering, Transactions of the ASME, Vol. 121(1), pp. 54-60. [18] Kubica E.G., Ismail F., 1996, “Active Suppression of Chatter in Peripheral Milling Part II: Application of Fuzzy Control,” International Journal of Advanced Manufacturing Technology, Vol. 12(4), pp. 236-245. [19] Altintas Y., and Chan P.K., 1992, “In-Process Detection and Suppression of Chatter in Milling,” International Journal of Machine Tools and Manufacture, Vol. 32(3), pp. 329-347.