Designing an Artificial Neural Network Based Model for Online Prediction of Tool Life in Turning

Subject Areas : Mechanical Engineering

A. Salimiasl ¹ , A. Özdemir ² , I. Safarian ³

1 - Department of Mechanical Engineering, Payame Noor University, Iran
2 - Department of Manufacturing Engineering, Faculty of Technology, University of Gazi, Ankara, Turkey
3 - Department of Mechanical Engineering, Payame Noor University, I.R. of Iran

Received: 2014-10-29 Accepted : 2015-02-15 Published : 2015-06-01

Keywords:

References:

[1]    Isabelle Guyon, A. E., “An introduction to variable and feature selection”, J. Mach. Learn. Res., Vol. 3, 2003, pp. 1157-1182.

[2]    Cho, D.-W., Lee, S. J., and Chu, C. N., “The state of machining process monitoring research in Korea”, International Journal of Machine Tools and Manufacture, Vol. 39, No. 11, 1999, pp. 1697-1715.

[3]    Liang, S. Y., Hecker, R. L., and Landers, R. G., “Machining process monitoring and control: The state-of-the-art”, Journal of Manufacturing Science and Engineering-Transactions of the Asme, Vol. 126, No. 2, May 2004, pp. 297-310.

[4]    Bahr, B., Motavalli, S., and Arfi, T., “Sensor fusion for monitoring machine tool conditions”, International Journal of Computer Integrated Manufacturing, Vol. 10, No. 5, 1997/01/01 1997, pp. 314-323.

[5]    Ertekin, Y. M., Kwon, Y., and Tseng, T.-L., “Identification of common sensory features for the control of CNC milling operations under varying cutting conditions”, International Journal of Machine Tools and Manufacture, Vol. 43, No. 9, 2003, pp. 897-904.

[6]    Zhang, J. Z. Chen, J. C., “The development of an in-process surface roughness adaptive control system in end milling operations”, International Journal of Advanced Manufacturing Technology, Vol. 31, No. 9-10, 2007, pp. 877-887.

[7]    Benardos, P. G. Vosniakos, G. C., “Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments”, Robotics and Computer-Integrated Manufacturing, Vol. 18, No. 5-6, 2002, pp. 343-354.

[8]    Niu, Y., Wong, Y., and Hong, G., “An intelligent sensor system approach for reliable tool flank wear recognition”, The International Journal of Advanced Manufacturing Technology, Vol. 14, No. 2, 1998, pp. 77-84.

[9]    Jantunen, E., “A summary of methods applied to tool condition monitoring in drilling”, International Journal of Machine Tools and Manufacture, Vol. 42, No. 9, 2002, pp. 997-1010.

[10]  Teti, R., Jemielniak, K., O'Donnell, G., and Dornfeld, D., “Advanced monitoring of machining operations”, Cirp Annals-Manufacturing Technology, Vol. 59, No. 2, 2010, pp. 717-739.

[11]  U. Zuperl, F. C., J. Balic. “Intelligent cutting tool condition monitoring in milling”, Journal of Achievements in Materials and Manufacturing Engineering, Vol. 49, No. 2, 2011, pp. 477-486.

[12]  Cakir, M. C. Isik, Y., “Detecting tool breakage in turning aisi 1050 steel using coated and uncoated cutting tools”, Journal of Materials Processing Technology, Vol. 159, No. 2, 2005, pp. 191-198.

[13]  Scheffer, C., Kratz, H., Heyns, P. S., and Klocke, F., “Development of a tool wear-monitoring system for hard turning”, International Journal of Machine Tools and Manufacture, Vol. 43, No. 10, 2003, pp. 973-985.

[14]  Gajate, A., Haber, R., del Toro, R., Vega, P., and Bustillo, A., “Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process”, Journal of Intelligent Manufacturing, Vol. 23, No. 3, 2012/06/01 2012, pp. 869-882.

[15]  Sharma, V., Sharma, S. K., and Sharma, A., “Cutting tool wear estimation for turning”, Journal of Intelligent Manufacturing, Vol. 19, No. 1, 2008/02/01 2008, pp. 99-108.

[16]  Haykin., S., Neural Networks: A Comprehensive Foundation, 1999, pp. 156-254.

[17]  RH, N., “Kolmogrov’s mapping neural network existence theorem”, in Second IEEE International Conference on Neural Networks, San Diego, June 21-24, 1987, pp. 11-14.

[18]  Achanta AS, K. I., Rhodes CT, Artificial neural networks: implications for pharmaceutical sciences, 1995.

[19]  Baughman DR, L. Y., Neural Networks in Bioprocessing and Chemical Engineering,New York, 1995.

[20]  RJ, E., Introduction to backpropagation neural network computation, 1993, pp. 165-170.

[21]         RH, N., “Kolmogrov’s mapping neural network existence theorem”, in Second IEEE International Conference on Neural Networks, San Diego, June 21-24,1987.