Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Science and Technology Planning Project of the State Administration of Market Supervision and Administration, China
Sichuan Science and Technology Program of China
Publisher
Springer Science and Business Media LLC
Reference44 articles.
1. An, Q., Tao, Z., Xu, X., El Mansori, M., & Chen, M. (2020). A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network. Measurement, 154, 107461. https://doi.org/10.1016/j.measurement.2019.107461
2. Bakhshi, A., Chalup, S., & Noman, N. (2020). Fast Evolution of CNN Architecture for Image Classification. In H. Iba & N. Noman (Eds.), Deep Neural Evolution: Deep Learning with Evolutionary Computation (pp. 209–229). Singapore: Springer. https://doi.org/10.1007/978-981-15-3685-4_8
3. Bergstra, J., Bardenet, R., Bengio, Y., & Kégl, B. (2011). Algorithms for Hyper-Parameter Optimization. In Advances in Neural Information Processing Systems (Vol. 24). Curran Associates, Inc. Retrieved May 17, 2023, from https://proceedings.neurips.cc/paper_files/paper/2011/hash/86e8f7ab32cfd12577bc2619bc635690-Abstract.html.
4. Castejón, M., Alegre, E., Barreiro, J., & Hernández, L. K. (2007). On-line tool wear monitoring using geometric descriptors from digital images. International Journal of Machine Tools and Manufacture, 47(12–13), 1847–1853. https://doi.org/10.1016/j.ijmachtools.2007.04.001
5. Chaowen, Z., Jing, J., & chi, C. (2021). Research On Tool Wear Monitoring Based On GRU-CNN. In 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) (pp. 729–733). Presented at the 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP). https://doi.org/10.1109/ICSP51882.2021.9408717