Open Access
Issue |
MATEC Web Conf.
Volume 125, 2017
21st International Conference on Circuits, Systems, Communications and Computers (CSCC 2017)
|
|
---|---|---|
Article Number | 04002 | |
Number of page(s) | 6 | |
Section | Computers | |
DOI | https://doi.org/10.1051/matecconf/201712504002 | |
Published online | 04 October 2017 |
- C. Scheffer, H. Kratz, P.S. Heyns, F. Klocke, Development of a tool wear-monitoring system for hard turning, International Journal of Machine Tools & Manufacture, 43, 973–985 (2003). [CrossRef] [Google Scholar]
- D. E. Dimla, P. M. Lister, On-line metal cutting tool condition monitoring. I: force, vibration analyses, International Journal of Machine Tools & Manufacture, 40, 739–768 (2000). [CrossRef] [Google Scholar]
- K. Jemielniak, T. Urbański, J. Kossakowska J., S. Bombiński, Tool condition monitoring based on numerous signal features, Int J. Adv. Manuf. Technol., 59, 73–81 (2012). [CrossRef] [Google Scholar]
- N. H. Abu-Zahra, G. Yu, Gradual wear monitoring of turning inserts using wavelet analysis of ultrasound waves, International Journal of Machine Tools & Manufacture, 43, 33–343 (2003). [Google Scholar]
- R. Lemaster, L. Lu, S. Jackson, The use of process monitoring techniques on a CNC wood router. Part 1. Sensor selection, Forest Products Journal, 50, 7/8, 31–64 (2000). [Google Scholar]
- J. Wilkowski, J. Górski, Vibro-acoustic signals as a source of information about tool wear during laminated chipboard milling, Wood Research, 56, 1, 57–66 (2011). [Google Scholar]
- R. G. Silva, K. J. Baker, S. J. Wilcox, The adaptability of a tool wear monitoring system under changing cutting conditions, Mechanical Systems, Signal Processing, 14, 2, 287–298 (2000). [CrossRef] [EDP Sciences] [Google Scholar]
- R. J. Kuo, Multi-sensor integration for on-line tool wear estimation through artificial neural networks, fuzzy neural network, Engineering Applications of Artificial Intelligence, 13, 249–261 (2000). [CrossRef] [Google Scholar]
- A. Noori-Khajavi, R. Komandur, Frequency, time domain analyses of sensor signals in drilling-Part I, International Journal of Machine Tools, Manufacture, 35, 6, 775–793 (1995). [Google Scholar]
- J. H. Zhou, C. K. Pang, Z. W. Zhong, F. L. Lewis, Tool wear monitoring using acoustic emissions by dominant-feature identification, IEEE Transactions on Instrumentation, Measurement, 60, 2, 547–559 (2011). [Google Scholar]
- S. S. Panda, A. K. Singh, D. Chakraborty, S. K. Pal, Drill wear monitoring using back propagation neural network, Journal of Materials Processing Technology, 172, 283–290 (2006). [CrossRef] [Google Scholar]
- K. Patra, S. K. Pal, K. Bhattacharyya, Artificial neural network based prediction of drill flank wear from motor current signals, Applied Soft Computing, 7, 929–935 (2007). [CrossRef] [Google Scholar]
- P. Lezanski, An intelligent system for grinding wheel condition monitoring, Journal of Materials Processing Technology, 109, 258–263 (2001). [CrossRef] [Google Scholar]
- H. Lutkepohl, Introduction to Multiple Time Series Analysis, Berlin Springer-Verlag (1991). [CrossRef] [Google Scholar]
- Matlab user manual, Natick: MathWorks, (2014). [Google Scholar]
- P.N. Tan, M. Steinbach, V. Kumar, Introduction to data mining, Boston: Pearson Education Inc. (2006). [Google Scholar]
- J. Kurek, S. Osowski, Support vector machine for fault diagnosis of the broken rotor bars of squirrel-cage induction motor, Neural Computing and Applications, 19, 557–564 (2010). [CrossRef] [Google Scholar]
- V. Kecman, Learning, Soft Computing: Support Vector Machines, Neural Networks,, Fuzzy Logic Models, Cambridge, MA: MIT Press (2001). [Google Scholar]
- M. Kruk, A. Jegorowa, J. Kurek, S. Osowski, J. Górski, Automatic recognition of drill condition on the basis of images of drilled holes, in Proc. Conference “Computational Problems of Electrical Engineering”, Sandomierz, 2016, pp. 1–4 [Google Scholar]
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