Tool life prediction in end milling using a combination of machining simulation and tool wear progress data
Author:
Affiliation:
1. Kobe University, Graduate School of Engineering, Department of Mechanical Engineering
Publisher
Japan Society of Mechanical Engineers
Subject
Industrial and Manufacturing Engineering,Mechanical Engineering
Link
https://www.jstage.jst.go.jp/article/jamdsm/17/2/17_2023jamdsm0025/_pdf
Reference9 articles.
1. Doukasa, C., Stavropoulosa, P., Papacharalampopoulosa, A., Foteinopoulosa, P., Vasiliadisa, E. and Chryssolouris, G., On the estimation of tool-wear for milling operations based on multisensorial data, Procedia CIRP, Vol. 8 (2013), pp. 415-420
2. Ezugwu, E., Arthur, S. and Hines, E., Tool-wear prediction using artificial neural networks, Journal of Materials Processing Technology, Vol. 49, No. 3-4 (1995), pp. 255-264
3. Guo, K., Yang, B., Sun, J. and Sivalingam, V., Investigation on the tool wear model and equivalent tool life in end milling titanium alloy TI6AL4V, MSEC2018 (2018)
4. Iwabe, H., Yamaguchi, K., Shimizu, K. and Nakanishi, K., On tool life and surface finish roughness in high-speed machining of hard materials with small-diameter ball end mills, Transactions of the Japan Society of Mechanical Engineers, Series C, Vol. 69, No. 687 (2003), pp. 3116-3123 (in Japanese)
5. Nouri, M., Fussell, B.K., Zinti, B.L. and Linder, E., Real-time tool wear monitoring in milling using a cutting condition independent method, International Journal of Machine Tools & Manufacturing, Vol. 89 (2015), pp. 1-13
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