A practical method to monitor tool wear in end milling using a changing cutting force model that requires no additional sensors
Author:
Affiliation:
1. Graduate School of Science and Engineering, Ibaraki University
2. Graduate School of Engineering, Kobe University
Publisher
Japan Society of Mechanical Engineers
Subject
Industrial and Manufacturing Engineering,Mechanical Engineering
Link
https://www.jstage.jst.go.jp/article/jamdsm/15/6/15_2021jamdsm0077/_pdf
Reference13 articles.
1. Drouillet, C., Karandikar, J, Nath, C., Journeaux, A., Mansori and M. Kurfess. T., Tool life predictions in milling using spindle power with the neural network technique, Journal of Manufacturing Processes, Vol. 22, (2016) pp.161-168.
2. Kaneko, K., Nishida, I., Sato, R. and Shirase, K., Machining state monitoring in end milling based on comparison of monitored and predicted cutting torques, Journal of Advanced Mechanical Design, Systems, and Manufacturing, Vol. 13, No. 3, (2019), DOI:10.1299/jamdsm.2019jamdsm0052.
3. Kim, G., Cho, P. and Chu, C., Cutting force prediction of sculptured surface ball-end milling using Z-map, International Journal of Machine Tools and Manufacture, Vol. 40, No. 2, (2000), pp. 277-291.
4. Kim, J., Moon, D., Lee, D., Kim, J., Kang, M and Kim, K.H, Tool wear measuring technique on the machine using CCD and exclusive jig, Journal of Materials Processing Technology, Vol. 130-131, (2002), pp. 668-674.
5. Kwon, Y. and Fischer, G., A novel approach to quantifying tool wear and tool life measurements for optimal tool management, International Journal of Machine Tools & Manufacture, Vol. 43, No. 4, (2003), pp. 359-368.
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