Research on the Processing Method of Tool Sensor Signal
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-9338-1_18
Reference13 articles.
1. Nie, P., Ma, Y., Zhang, K.F., et al.: Research status and development of tool wear monitoring technology. Tool Eng. 55(06), 3–12 (2021)
2. Zhou, H., Zhang, Z.N.: Feature transfer-based approach for tool wear monitoring of face milling. Tribology 121, 723–716 (2021). https://doi.org/10.16078/j.tribology.2021158
3. Vetrichelvan, G., Sundaram, S., Kumaran, S., et al.: An investigation of tool wear using acoustic emission and genetic algorithm. J. Vib. Control 21(15), 3061–3066 (2015)
4. Wu, Y.; Hong, G.S.; Wong, W.S.: Prognosis of the probability of failure in tool condition monitoring application-a time series-based approach. Int. J. Adv. Manuf. Technol. 76(1), 513–521 (2018)
5. Yu, J., Liang, S., Tang, D., Liu, H.: A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction. Int. J. Adv. Manuf. Technol. 91(1–4), 201–211 (2016). https://doi.org/10.1007/s00170-016-9711-0
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