Construction of Tool Life Dynamic Prediction Model Based on On-Line Learning

Author:

Duan Jicheng,Zhang Kechang

Publisher

Springer Nature Singapore

Reference14 articles.

1. Li, W.B.: Study on the essence and influence of Industrial Revolution 4.0. China University of Mining and Technology. Master's thesis, Xuzhou (2019)

2. Yan, S., Hu, X.F., Liu, Y.C., et al.: Study on the self-adaptive prediction method for the life of semi-finish milling cutter for wheel groove. Mod. Mach. Tools Autom. Mach. Technol. 2016(9), 95–98 (2016)

3. Wang, Z.W., Zhou, Y.J.: Research on key technology of tool wear monitoring system for NC machine tool. Internal Combust. Eng. Accessories 2021(1), 59–60 (2021)

4. Tian, Y., Wang, W.H., Yang, L.M., Shao, W.T.: Multi-objective optimization of machining parameters based on tool wear state identification. J. Chin. Tianjin Univ. 55(2), 166–173 (2022)

5. Wang, G.F., Dong, Y., Yang, K., et al.: Tool life prediction based on depth learning and particle filter. J. Tianjin Univ. Sci. Eng. 52(11), 1109–1116 (2019)

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