MNSMS  Vol.2 No.2 , April 2012
Tool Wear Classification Using Fuzzy Logic for Machining of Al/SiC Composite Material
Abstract: Tool wear state classification has good potential to play a critical role in ensuring the dimensional accuracy of the work piece and prevention of damage to cutting tool in machining process. During machining process, tool wear is an important factor which contributes to the variation of spindle motor current, speed, feed and depth of cut. In the present work, online tool wear state detecting method with spindle motor current in turning operation for Al/SiC composite material is presented. By analyzing the effects of tool wear as well as the cutting parameters on the current signal, the models on the relationship between the current signals and the cutting parameters are established with partial design taken from experimental data and regression analysis. The fuzzy classification method is used to classify the tool wear states so as to facilitate defective tool replacement at the proper time.
Cite this paper: V. Kalaichelvi, R. Karthikeyan, D. Sivakumar and V. Srinivasan, "Tool Wear Classification Using Fuzzy Logic for Machining of Al/SiC Composite Material," Modeling and Numerical Simulation of Material Science, Vol. 2 No. 2, 2012, pp. 28-36. doi: 10.4236/mnsms.2012.22003.

[1]   X. Li, S. Dong and P. K. Venuvinod, “Hybrid Learning for Tool Wear Monitoring,” International Journal of Ad vanced Manufacturing Technology, Vol. 16, No. 5, 2000, pp. 303-307. doi:10.1007/s001700050161

[2]   L. C. Lee, K. S. Lee and C. S. Gan, “On the Correlation between Dynamic Cutting Force and Tool Wear,” International Journal of Machine Tools and Manufacture, Vol. 29, No. 3, 1989, pp. 295-303. doi:10.1016/0890-6955(89)90001-1

[3]   S. B. Rao, “Metal Cutting Machine Tool Design—A Review,” Journal of Manufacturing Science and Engineering Transactions of ASME, Vol. 119, No. 4, 1997, pp. 713-716. doi:10.1115/1.2836814

[4]   K. Danai and A. G. Ulsoy, “Dynamic State Model for On-Line Tool Wear Estimation in Turning,” Journal of Engineering for Industry, Transactions of ASME, Vol. 109, No. 4, 1987, pp. 396-399. doi:10.1115/1.3187145

[5]   L. Dan and J. Mathew, “Tool Wear and Failure Monitoring Techniques for Turning—A Review,” International Journal of Machine Tools and Manufacture, Vol. 30, No. 4, 1990, pp. 579-598. doi:10.1016/0890-6955(90)90009-8

[6]   X. L. Li and S. K. Tso, “Drill Wear Monitoring Based on Current Signals,” Wear, Vol. 231, No. 2, 1999, pp. 172- 178. doi:10.1016/S0043-1648(99)00130-1

[7]   M. A. Mannan, S. Broms and B. Lindstrom, “Monitoring and Adaptive Control of Cutting Process by Means of Motor Power and Current Measurements,” CIRP Annals—Manufacturing Technology, Vol. 38, No. 1, 1989, pp. 347-350. doi:10.1016/S0007-8506(07)62720-6

[8]   M. A. Mannan and T. Nilsson, “The Behavior of Static Torque and Thrust Due to Tool Wear in Drilling,” Technical Papers of the North American Manufacturing Research Institution of ASME, 1997, pp. 75-80.

[9]   G. Byrne, D. Dornfeld, I. Inasaki, G. Ketteler, W. Konig and R. Teti, “Tool Condition Monitoring (TCM)—The Status of Research and Industrial Application,” CIRP An-nals—Manufacturing Technology, Vol. 44, No. 2, 1995, pp. 541-567. doi:10.1016/S0007-8506(07)60503-4

[10]   Y. Koren, T. Ko and A. G. Ulsoy, “Flank Wear Estimation under Varying Cutting Conditions,” Journal of Dynamic Systems, Measurement and Control, Vol. 113, No. 2, 1991, pp. 300-307.

[11]   J. L. Devore and N. R. Farnum, “Applied Statistics for Engineers and Scientists,” 2nd Edition, Duxbury Press, Belmont, 2004.

[12]   J. S. R. Jang, “Adaptive Network Based Fuzzy Inference Systems,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 23, No. 3, 1999, pp. 665-685.

[13]   Q. Liu and Y. Altintas, “On-Line Monitoring of Flank Wear in Turning with Multilayered Feed-Forward Neural Network,” International Journal of Machine Tools and Manufacture, Vol. 39, No. 12, 1999, pp. 1945-1959. doi:10.1016/S0890-6955(99)00020-6

[14]   Y. X. Yao, X. L. Li and Z. J. Yuan, “Tool Wear Detection with Fuzzy Classification and Wavelet Fuzzy Neural Network,” International Journal of Machine Tools and Manufacture, Vol. 39, No. 10, 1999, pp. 1525-1538. doi:10.1016/S0890-6955(99)00018-8

[15]   M. C Shaw, “Metal Cutting Principles,” Clarendon Press, Oxford, 1984.

[16]   X. L. Li, “Real Time Tool Condition Monitoring in Turning,” International Journal of Production Research, Vol. 30, No. 5, 2001, pp. 981-992.