Remote Monitoring for Surface Roughness Based on Vibration and Spindle Power-Reference-Cited by-同舟云学术

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Remote Monitoring for Surface Roughness Based on Vibration and Spindle Power

Published:2022-05-11 Issue:3 Volume:48 Page:2617-2631
ISSN:2193-567X
Container-title:Arabian Journal for Science and Engineering
language:en
Short-container-title:Arab J Sci Eng

Author:

Wu Leibo^ORCID,Fan Kaiguo,Le Wen

Funder

Technology of online monitoring system for thermal characteristics of NC machine tools

Project funded of Shanghai science committee-Precision technology and its application for five-axis machine tool based on the real-time compensation

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

https://link.springer.com/content/pdf/10.1007/s13369-022-06879-2.pdf

Reference33 articles.

1. Şahinoğlu, A.; Rafighi, M.: Investigation of vibration, sound intensity, machine current and surface roughness values of AISI 4140 during machining on the lathe. Arab. J. Sci. Eng. 45, 765–778 (2020). https://doi.org/10.1007/s13369-019-04124-x

2. Lu, C.; Ma, N.; Chen, Z.; Philippe, C.Z.: Pre-evaluationon surface profile in turning process based on cutting parameters. Int. J. Adv. Manuf. Technol. 49(5), 447–458 (2020). https://doi.org/10.1007/s00170-009-2417-9

3. Lela, B.; Baji, D.; Jozi, S.: Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling. Int. J. Adv. Manuf. Technol. 42(11–12), 1082–1088 (2009). https://doi.org/10.1007/s00170-008-1678-z

4. Lin, Y.; Chen, Y.; Wu, K.; Hung, J.: Prediction of surface roughness based on the machining conditions with the effect of machining stability. Adv. Sci. Technol. Res. J. 14 (2),171–83 (2020). https://doi.org/10.12913/22998624/119048

5. Mia, M.; Morshed, M.S.; Kharshiduzzaman, M.; Razi, M.H.; Mostafa, M.R.; Rahman, S.M.S.; Ahmad, I.; Hafiz, M.T.; Kamal, A.M.: Prediction and optimization of surface roughness in minimum quantity coolant lubrication applied turning of high hardness steel. Measurement 118, 43–51 (2018). https://doi.org/10.1016/j.measurement.2018.01.012

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1. Energy efficiency identification and surface roughness prediction using cutting force signal for computer numerical controlled machine systems;Scientific Reports;2024-08-16

2. Precision enhancement in CNC face milling through vibration-aided AI prediction of surface roughness;International Journal on Interactive Design and Manufacturing (IJIDeM);2024-07-02

3. Quality monitoring solution: measurement and modeling of product external diameter in CNC turning;The International Journal of Advanced Manufacturing Technology;2024-06-19

4. Surface roughness estimation using vibration characteristics extracted by variational mode decomposition in turning;Engineering Research Express;2024-06-01

5. Beyond Homophily and Homogeneity Assumption: Relation-Based Frequency Adaptive Graph Neural Networks;IEEE Transactions on Neural Networks and Learning Systems;2023

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