Tool Wear Estimation using Support Vector Machines in Ball-nose End Milling
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
This paper introduces a method to determine the tool wear by measured cutting force in Ball-nose End Milling. The features will be extracted from the measured cutting force with different flank wear. As the adaptive window width in wavelet transform is an advantage for analyzing and monitoring the rapid transient of small amplitude of cutting force signals when cutting engagement changes along the sculptured surface tool path, wavelet transform (WT) is more effective than FFT monitoring index for ball-nose end milling. In this research, cutting force signals will be analyzed in time-frequency domain to explore sensitive monitoring features in ball-nose end milling slope surfaces. As a supervised method, support vector machines (SVM) was developed for the classification problem to take advantage of prior knowledge of tool wear and construct a hyper- plane as the decision surface. In this paper, SVM will be formulated into regression problem to estimate tool wear rather than decision maker.
How to Cite
##plugins.themes.bootstrap3.article.details##
support vector machines, Tool condition monitoring
Bhattacharyya, P., Sengupta, D. and Mukhopadhyay, S. (2007). Cutting force-based real-time estimation of tool wear in face milling using a combination of signal processing techniques, Mechanical Systems and Signal Processing, vol. 21, No. 6, pp. 2665- 2683.
Chang, C. and Lin, C. (2001). LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
Cho, S., Asfour, S., Onar, A. and Kaundinya, N. (2005). Tool breakage detection using support vector machine learning in a milling process. International Journal of Machine Tools and Manufacture, vol. 45, No. 3, pp 241-249.
Choi, Y., Narayanaswami, R., Chandra, A. (2004). Tool wear monitoring in ramp cuts in end milling using the wavelet transform, Int J Adv Manuf Technol, vol. 23, pp. 419–428.
Dong, J., Subrahmanyam, K., Wong, Y.S., Hong, G.S. and Mohanty, A. (2006). Bayesian-inference-based neural networks for tool wear estimation, The International Journal of Advanced Manufacturing Technology, vol. 30, No.9, pp. 797-807.
Gaing, Z. L. (2004). Wavelet-Based Neural Network for Power Disturbance Recognition and Classification, IEEE Transactions on Power Delivery, Vol. 19, No. 4, pp. 1560-1568.
Ghosh, N., Ravi, Y.B., Patra, A., Mukhopadhyay, S., Paul, S., Mohanty, A.R. and Chattopadhyay, A.B. (2007). Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mechanical Systems and Signal Processing, vol. 21, No. 1, pp. 466-479.
Haykin, S. (1999). Neural Networks-a Comprehensive Foundation, Prentice Hall, New Jersey.
Hong, G. S., Rahman, M., and Zhou, Q. (1996). Using Neural Network for Tool Condition Monitoring Based on Wavelet Decomposition, Int. J. Mach. Tools Manufact. Vol. 36, No. 5, pp. 551-566.
Li, X., Lim, B.S., Zhou, J.H., Huang, S., Phua, S.J., Shaw, K.C. (2009). Fuzzy Neural Network Modelling for Tool Wear Estimation in Dry Milling Operation, Annual Conference of the Prognostics and Health Management Society.
Prickett, P.W., Johns, C. (1999). An overview of approaches to end milling tool monitoring, International Journal of Machine Tools & Manufacture, vol. 39, pp. 105–122.
Sun, J., Hong, G.S. Wong, Y.S., Rahman, M., Wang, Z.G. (2006). Effective training data selection in tool condition monitoring system, International Journal of Machine Tools & Manufacture, vol. 46, pp. 218– 224.
Widodo, A. and Yang, B.-S. (2007). Support vector machine in machine condition monitoring and fault diagnosis, Mechanical Systems and Signal Processing, vol. 21, No.6, pp. 2560-2574.
Zhou, J.-H., Pang, C.K., Lewis, F.L. and Zhong, Z.-W. (2009). Intelligent Diagnosis and Prognosis of Tool Wear Using Dominant Feature Identification, IEEE Transactions on Industrial Informatics, vol. 5, No. 4, pp. 454-464.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.