Tool Wear Monitoring Using Multi-sensor Time Series and Machine Learning
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-49011-8_39
Reference23 articles.
1. Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019). https://doi.org/10.1145/3292500.3330701
2. Arslan, M., Kamal, K., Sheikh, M.F., Khan, M.A., Ratlamwala, T.A.H., Hussain, G., Alkahtani, M.: Tool health monitoring using airborne acoustic emission and convolutional neural networks: a deep learning approach. Appl. Sci. 11(6), 2734 (2021). https://doi.org/10.3390/app11062734
3. Cao, X., Chen, B., Yao, B., Zhuang, S.: An intelligent milling tool wear monitoring methodology based on convolutional neural network with derived wavelet frames coefficient. Appl. Sci. 9(18), 3912 (2019). https://doi.org/10.3390/app9183912
4. Carrino, S., Guerne, J., Dreyer, J., Ghorbel, H., Schorderet, A., Montavon, R.: Machining quality prediction using acoustic sensors and machine learning. Proceedings 63(1), 31 (2020). https://doi.org/10.3390/proceedings2020063031
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