Acoustic emission reflection signal classification of PVDF-type AE sensor using convolutional neural network-transfer learning
Author:
Funder
Ministry of Science and ICT, South Korea
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Link
https://link.springer.com/content/pdf/10.1007/s10845-023-02263-5.pdf
Reference40 articles.
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2. Barile, C. (2019). Innovative mechanical characterization of CFRP by using acoustic emission technique. Engineering Fracture Mechanics, 210, 414–421. https://doi.org/10.1016/j.engfracmech.2018.02.024
3. Bhuiyan, M., Choudhury, I. A., Dahari, M., Nukman, Y., & Dawal, S. (2016). Application of acoustic emission sensor to investigate the frequency of tool wear and plastic deformation in tool condition monitoring. Measurement, 92, 208–217. https://doi.org/10.1016/j.measurement.2016.06.006
4. Bi, G., Liu, S., Su, S., & Wang, Z. (2021). Diamond grinding wheel condition monitoring based on acoustic emission signals. Sensors, 21(4), 1054. https://doi.org/10.3390/s21041054
5. Bianchi, D., Mayrhofer, E., Gröschl, M., Betz, G., & Vernes, A. (2015). Wavelet packet transform for detection of single events in acoustic emission signals. Mechanical Systems and Signal Processing, 64, 441–451. https://doi.org/10.1016/j.ymssp.2015.04.014
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