A Generative AI approach to improve in-situ vision tool wear monitoring with scarce data
Author:
Funder
HORIZON EUROPE Framework Programme
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10845-024-02379-2.pdf
Reference102 articles.
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3. Bagga, P. J., Makhesana, M. A., Patel, K., & Patel, K. M. (2021). Tool wear monitoring in turning using image processing techniques. Materials Today: Proceedings, 44, 771–775. https://doi.org/10.1016/j.matpr.2020.10.680
4. Banda, T., Farid, A. A., Li, C., Jauw, V. L., & Lim, C. S. (2022). Application of machine vision for tool condition monitoring and tool performance optimization-a review. International Journal of Advanced Manufacturing Technology, 121(11), 7057–7086. https://doi.org/10.1007/s00170-022-09696-x
5. Barreiro, J., Fernández-Abia, A. I., González-Laguna, A., & Pereira, O. (2017). TCM system in contour milling of very thick-very large steel plates based on vibration and AE signals. Journal of Materials Processing Technology, 246, 144–157. https://doi.org/10.1016/j.jmatprotec.2017.03.016
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