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Intelligent identification of cutting states by utilising Power Spectrum Density

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: This paper presents an in-process monitoring and identification of cutting states in turning process in order to realize the intelligent machine tools. Design/methodology/approach: The developed method utilizes the power spectrum density, or PSD of dynamic cutting force measured during the cutting. The experimentally obtained results suggested that there are basically three types of patterns of PSD when the cutting states are the continuous chip formation, the broken chip formation, and the chatter. The broken chip formation is desired to realize safe and reliable machining. The method proposed introduces three ratios, which are calculated and obtained by taking the ratios of cumulative PSD for a certain frequency range of three dynamic cutting force components corresponding to those three states of cutting, to classify the cutting states. The algorithm was developed to calculate the values of three ratios during the process in order to obtain the proper threshold values for classification of the cutting states. Findings: The method developed has been proved by series of cutting experiments that the states of cutting are well identified regardless of the cutting conditions. The broken chips are easily obtained by changing the cutting conditions during the processes referring to the algorithm developed. Practical implications: There are still not many systems being used in practice mainly due to the lack of general applicability such as a requirement of automated machining systems regardless of the cutting conditions. The aim of this research is to develop an in-process monitoring system for identification of continuous chip, broken chip, and chatter regardless of the cutting conditions by spectrum analysis based on the power spectrum density, or PSD of dynamic cutting force measured during the cutting. Originality/value: The largest potential advantage of the method proposed in this paper is that the states of cutting can be readily identified during the in-process cutting under any cutting conditions by simply mapping the experimentally obtained values of parameters referring to the proper threshold values in the reference feature spaces.
Rocznik
Strony
580--587
Opis fizyczny
Bibliogr. 19 poz., wykr., tab., il.
Twórcy
  • Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, 10330 Thailand, somkiat.t@eng.chula.ac.th
Bibliografia
  • [1] T. Moriwaki, T. Somkiat, T. Shibasaka, Intelligent monitoring and optimization of cutting process for CNC turning, Proceedings of the 14th International Conference „Flexible Automation and Intelligent Manufacturing”, Toronto, 2004, 82-89.
  • [2] W. Grzesik, P. Bernat, An investigation of the cutting process for chip breaking monitoring in turning of steels, Journal of Manufacturing Science and Engineering 120 (1998) 555-562.
  • [3] J. L. Andreasen, L. D. Chiffre, Automatic chip-breaking detection in turning by frequency analysis of cutting force, Annals of the CIRP 42 (1993) 45-48.
  • [4] I. S. Jawahir, C. A. Luttervelt, Recent developments in chip control research and applications, Annals of the CIRP 42 (1993) 659-685.
  • [5] S. Sawai, I. Inasaki, Identification of chip form in turning process, International Journal of Japan Society of Mechanical Engineers 34 (1991) 553-560.
  • [6] Y. Altintas, P. K. Chan, In-process detection and suppression of chatter in milling, International Journal of Machine Tools and Manufacture 32 (1992) 329-347.
  • [7] M. Rahman, In-process detection of chatter threshold, Journal of Engineering for Industry 110 (1988) 44-50.
  • [8] J. Hino, C. Su, T. Yoshimura, A study on chatter prediction in high-speed end milling process by fuzzy neural network, International Journal of Japan Society of Mechanical Engineers C 47 (2001) 825-831.
  • [9] T. Moriwaki, T. Shibasaka, T. Somkiat, Development of inprocess chip-breaking detection system in turning, Memoirs of graduate school of science and technology, Kobe University, 22-A, 2004, 1-19.
  • [10] T. Moriwaki, Y. Mori, Sensor fusion for in-process identification of cutting process based on neural network approach, Proceedings of the IMACS/SICE International Symposium „Robotics, Mechatronics and Manufacturing systems”, Kobe, 1992, 245-250.
  • [11] K. Matsushima, T. Sata, On-line control of the cutting state by the pattern recognition technique, Annals of the CIRP 23 (1974) 151-152.
  • [12] T. Sata, K. Matsushima, T. Nagamura, E. Kono, Learning and recognition of the cutting states by the spectrum analysis, Annals of the CIRP 22 (1973) 41-42.
  • [13] K. Ueda, S. Miyamoto, T. Sugita, An approach to adaptive control based on pattern recognition of cutting states, Proceedings of the 6th International Conference „Production Engineering”, Osaka, 1987, 212-217.
  • [14] L. C. Lee, K. S. Lee, C. S. Gan, On the correlation between dynamic cutting force and tool wear, International Journal of Machine Tools and Manufacture 29 (1989) 295-303.
  • [15] J. D. Kim, O. B. Kweun, A chip-breaking system for mild steel in turning, International Journal of Machine Tools and Manufacture 37 (1997) 607-617.
  • [16] X. D. Fang, J. Fei, I. S. Jawahir, A hybrid algorithm for predicting chip form/chip breakability in machining, International Journal of Machine Tools and Manufacture 36 (1996) 1093-1107.
  • [17] R. Teti, I. S. Jawahir, K. Jemielniak, S. Segreto, S. Chen, J. Kossakowska, Chip form monitoring through advanced processing of cutting force sensor signals, Annals of the CIRP 55/1 (2006) 75-80.
  • [18] M. C. Shaw, Metal cutting principles, Second Edition, Oxford University Press, 2005, 494-497.
  • [19] C. Chungchoo, D. Saini, The total energy and the total entropy of force signals-new parameters for monitoring oblique turning operations, International Journal of Machine Tools and Manufacture 40 (2000) 1879-1897.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWAN-0004-0013
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