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IJAT Vol.7 No.4 pp. 410-417
doi: 10.20965/ijat.2013.p0410
(2013)

Paper:

The Use of Machine Tool Internal Encoders as Sensors in a Process Monitoring System

Tomas Beno*, Jari Repo*, and Lars Pejryd**

*Department of Engineering Science, University West, Trollhättan 461 86, Sweden

**School of Science and Technology, Örebro University, Örebro 701 82, Sweden

Received:
February 1, 2013
Accepted:
May 31, 2013
Published:
July 5, 2013
Keywords:
milling, tool wear detection, encoder signals, monitoring system architecture
Abstract
Tool wear in machining changes the geometry of the cutting edges, which affects the direction and amplitudes of the cutting force components and the dynamics in the machining process. These changes in the forces and dynamics are picked up by the internal encoders and thus can be used for monitoring of changes in process conditions. This paper presents an approach for the monitoring of amulti-toothmilling process. The method is based on the direct measurement of the output from the position encoders available in the machine tool and the application of advanced signal analysis methods. The paper investigates repeatability of the developed method and discusses how to implement this in a process monitoring and control system. The results of this work show that various signal features which are correlated with tool wear can be extracted from the first few oscillating components, representing the low-frequency components, of the machine axes velocity signatures. The responses from the position encoders exhibit good repeatability, especially short term repeatability while the long-term repeatability is more unreliable.
Cite this article as:
T. Beno, J. Repo, and L. Pejryd, “The Use of Machine Tool Internal Encoders as Sensors in a Process Monitoring System,” Int. J. Automation Technol., Vol.7 No.4, pp. 410-417, 2013.
Data files:
References
  1. [1] Dimla Snr Dimla E., “Sensor signals for tool-wear monitoring in metal cutting operations – a review of methods,” Int. J. of Machine Tools and Manufacture, Vol.40, No.8, pp. 1073-1098, 2000.
  2. [2] M. Ritou, S. Garnier, B. Furet, and J.-Y. Hascoet, “A new versatile in-process monitoring system for milling,” Int. J. of Machine Tools and Manufacture, Vol.46, No.15, pp. 2026-2035, 2006.
  3. [3] W. Amer, R. I. Grosvenor, and P.W. Prickett, “Sweeping filters and tooth rotation energy estimation (tree) techniques for machine tool condition monitoring,” Int. J. of Machine Tools and Manufacture, Vol.46, No.9, pp. 1045-1052, 2006.
  4. [4] P. Bhattacharyya, D. Sengupta, and S. Mukhopadhyay, “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, 2007.
  5. [5] A. Rivero, L. N. López de Lacalle, and M. Luz Penalva, “Tool wear detection in dry high-speed milling based upon the analysis of machine internal signals,” Mechatronics, Vol.18, No.10, pp. 627-633, 2008.
  6. [6] X. Li, G. Ouyang, and Z. Liang, “Complexity measure of motor current signals for tool flute breakage detection in end milling,” Int. J. of Machine Tools and Manufacture, Vol.48, Issues 3-4, pp. 371-379, 2008.
  7. [7] F. Girardin, D. Rémond, and J.-F. Rigal, “Tool wear detection in milling – an original approach with a nondedicated sensor,” Mechanical Systems and Signal Processing, Vol.24, No.6, pp. 1907-1920, 2010, doi: 10.1016/j.ymssp.2010.02.008.
  8. [8] R. Teti, K. Jemielniak, G. ODonnell, and D. Dornfeld, “Advanced monitoring of machining operations,” CIRP Annals – Manufacturing Technology, Vol.59, No.2, pp. 717-739, 2010.
  9. [9] J. Repo, “Condition Monitoring in Machining Using Internal Sensor Signals,” Monography, Royal Institute of Technology, 2012.
  10. [10] J. Repo, L. Pejryd, and T. Beno, “Measurement method for the identification of individual teeth in milling operations,” CIRP J. of Manufacturing Science and Technology, Vol.5, No.1, pp. 26-32, 2012.
  11. [11] L. Pejryd, J. Repo, and T. Beno, “Machine tool internal encoders as sensors for the detection of tool wear,” In Procedia CIRP 4, Vol.4, pp. 46-51, 2012.
  12. [12] L. Jianwen, B. Jing, and S. Jinhua, “Application of the wavelet transforms on axial strain calculation in ultrasound elastography,” Progress in Natural Science, Vol.16, No.9, 2006.
  13. [13] J. B. Elsner and A. a. Tsonis, “Singular Spectrum Analysis: A New Tool in Time Series Analysis,” Springer, 2010.
  14. [14] X. Desforges and B. Archimde, “Multi-agent framework based on smart sensors/actuators for machine tools control and monitoring,” Engineering Applications of Artificial Intelligence, Vol.19, No.6, pp. 641-655, 2006.

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