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

Authors

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

Abstract

Artificial neural network is one of the most robust and reliable methods in online prediction of nonlinear incidents in machining. Tool flank wear as a tool life criterion is an important task which is needed to be predicted during machining processes to establish an online tool life estimation system.In this study, an artificial neural network model was developed to predict the tool wear and tool life in turning process. Cutting parameters and cutting forces were used as input and tool flank wear rates were regarded as target data for creating the online prediction system. SIMULINK and neural network tool boxes in MATLAB software were used for establishing a reliable online monitoring model. For generalizing the model, full factorial method was used to design the experiments. Predicted results were compared with the test results and a full confirmation of the model was reached.

Keywords


[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.