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Review of Tool Condition Monitoring Methods

by Ramesh Visariya, Ronak Ruparel, Rahul Yadav
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 37
Year of Publication: 2018
Authors: Ramesh Visariya, Ronak Ruparel, Rahul Yadav
10.5120/ijca2018916853

Ramesh Visariya, Ronak Ruparel, Rahul Yadav . Review of Tool Condition Monitoring Methods. International Journal of Computer Applications. 179, 37 ( Apr 2018), 29-32. DOI=10.5120/ijca2018916853

@article{ 10.5120/ijca2018916853,
author = { Ramesh Visariya, Ronak Ruparel, Rahul Yadav },
title = { Review of Tool Condition Monitoring Methods },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2018 },
volume = { 179 },
number = { 37 },
month = { Apr },
year = { 2018 },
issn = { 0975-8887 },
pages = { 29-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number37/29284-2018916853/ },
doi = { 10.5120/ijca2018916853 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:57:42.892678+05:30
%A Ramesh Visariya
%A Ronak Ruparel
%A Rahul Yadav
%T Review of Tool Condition Monitoring Methods
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 37
%P 29-32
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the increase of modern industries use of metal cutting procedure, it is evident that the tools used for this processes required proper care and monitoring. Tool wear one of the most important factors in machining processes as it greatly affects the tool life, which is important in metal cutting because of its direct impact on the quality of the finished job and also affect the efficiency of industries. Hence, ways to observe cutting tool and monitor its wear are needed for optimal use. An effective system can reduce machine downtime and economic losses. The paper presents a overview of many Tool Condition Monitoring Systems.

References
  1. Venkatesh, K., Zhou, M., & Caudill, R.J. Design of artificial neural networks for tool wear monitoring. Journal of Intelligent Manufacturing, 8(3), 1997, pp. 215-226.
  2. Li, X., Dong, S., & Venuvinod, P.K. Hybrid learning or tool wear monitoring. International Journal of Advanced Manufacturing Technology, 16, 2000, pp.303- 307.
  3. Lee, J.H., Kim, D.E., & Lee, S.J. Statistical analysis of cutting force ratios for flank-wear monitoring. Journal of Materials Processing Technology, 74, 1998, pp. 104-114.
  4. S. K. Sikdar, M. Chen, Relationship between tool flank wear area and component forces in single point turning, Journal of Materials Processing Technology 128 (2002) 210215.
  5. Lee S (2010) Tool condition monitoring system in turning operation utilizing wavelet signal processing and multi-learning ANNs algorithm methodology. Int J Eng Res Innov 49.
  6. Kurada, S., and C. Bradley. ”A review of machine vision sensors for tool condition monitoring.” Computers in industry 34.1 (1997): 55-72.
  7. Balazinski, Marek, et al. ”Tool condition monitoring using artificial intelligence methods.” Engineering Applications of Artificial Intelligence 15.1 (2002): 73-80.
  8. Zel, Tugrul, and Abhijit Nadgir. ”Prediction of flank wear by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN tools.” International Journal of Machine Tools and Manufacture 42.2 (2002): 287-297.
  9. Dimla Sr DE, Lister PM. On-line metal cutting tool condition monitoring. I: force and vibration analyses, International Journal of Machine Tools and Manufacture 2000; 40:739768.
  10. Abouelatta OB, Madl J. Surface roughness prediction based on cutting parameter and tool vibration in turning operation. Journal of material processing technology 2001; 118:269-277.
  11. Alonso FJ, Salgado DR, Analysis of the structure of vibration signals for tool wear detection. Mechanical Systems and Signal Processing 2008; 22:735748.
  12. Al-Habaibeh, A., and N. Gindy. ”A new approach for systematic design of condition monitoring systems for milling processes.” Journal of Materials Processing Technology 107.1-3 (2000): 243-251.
  13. Niu, Y. M., et al.”Multi-category classification of tool conditions using wavelet packets and ART2 network.” Journal of manufacturing science and engineering 120.4 (1998): 807-816.
  14. Chen, Shang-Liang, and Y. W. Jen. ”Data fusion neural network for tool condition monitoring in CNC milling machining.” International journal of machine tools and manufacture 40.3 (2000): 381-400.
  15. Kuo, R. J. ”Multi-sensor integration for on-line tool wear estimation through artificial neural networks and fuzzy neural network.” Engineering Applications of Artificial Intelligence 13.3 (2000): 249-261.
  16. Li, Xiaoli. ”Detection of tool flute breakage in end milling using feed-motor current signatures.” IEEE/ASME transactions on mechatronics 6.4 (2001): 491-498.
  17. Stein, Jeffrey L., and Kunsoo Huh. ”Monitoring cutting forces in turning: a model-based approach.” Journal of Manufacturing science and engineering 124.1 (2002): 26-31.
  18. Li, Xiaoli. ”Real-time tool wear condition monitoring in turning.” International Journal of Production Research 39.5 (2001): 981-992.
  19. Wu, Ya, and R. Du. ”Feature extraction and assessment using wavelet packets for monitoring of machining processes.” Mechanical systems and signal processing 10.1 (1996): 29-53.
  20. Tseng, P. C., and A. Chou. ”The intelligent on-line monitoring of end milling.” International Journal of Machine Tools and Manufacture 42.1 (2002): 89-97.
  21. Choudhury, S. K., and K. K. Kishore. ”Tool wear measurement in turning using force ratio.” International Journal of Machine Tools and Manufacture 40.6 (2000): 899-909.
  22. Davim, J.P., and Baptista, A.M. (2000). Relationship between cutting force and PCD cutting tool wear in machining silicon carbide reinforced aluminum. Journal of Materials Processing Technology, 103, 417-423.
  23. Zel, Turul, and Yiit Karpat. ”Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks.” International Journal of Machine Tools and Manufacture 45.4-5 (2005): 467-479.
  24. Li, Xiaoli. ”A brief review: acoustic emission method for tool wear monitoring during turning.” International Journal of Machine Tools and Manufacture 42.2 (2002): 157-165.
Index Terms

Computer Science
Information Sciences

Keywords

Decision Making Systems Condition Monitoring Neural Networks.

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