A data-driven method for prediction of surface roughness with consideration of milling tool wear
Author:
Funder
National Natural Science Foundation of China
Key Research and Development Projects of Shaanxi Province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00170-024-14381-2.pdf
Reference27 articles.
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2. Chen S, Zhao W, Yan P, Qiu T, Gu H, Jiao L, Wang X (2022) Effect of milling surface topography and texture direction on fatigue behavior of ZK61M magnesium alloy. Int J Fatigue 156:106669. https://doi.org/10.1016/j.ijfatigue.2021.106669
3. Zhang X, Yu T, Zhao J (2020) Surface generation modeling of micro milling process with stochastic tool wear. Precis Eng 61:170–181. https://doi.org/10.1016/j.precisioneng.2019.10.015
4. Bouhalais ML, Nouioua M (2021) The analysis of tool vibration signals by spectral kurtosis and ICEEMDAN modes energy for insert wear monitoring in turning operation. Int J Adv Manuf Technol 115:2989–3001. https://doi.org/10.1007/s00170-021-07319-5
5. Sun W, Zhang D, Luo M (2021) Machining process monitoring and application: a review. J Adv Manuf Sci Technol 1(2):2021001–2021001. https://doi.org/10.51393/j.jamst.2021001
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