Intelligent G-code-based power prediction of ultra-precision CNC machine tools through 1DCNN-LSTM-Attention model
Author:
Funder
Wisconsin Alumni Research Foundation
National Research Foundation of Korea
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10845-023-02293-z.pdf
Reference47 articles.
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4. Benkedjouh, T., Medjaher, K., Zerhouni, N., & Rechak, S. (2015). Health assessment and life prediction of cutting tools based on support vector regression. Journal of Intelligent Manufacturing, 26, 213–223. https://doi.org/10.1007/s10845-013-0774-6
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