Prediction of cutting force via machine learning: state of the art, challenges and potentials
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Link
https://link.springer.com/content/pdf/10.1007/s10845-023-02260-8.pdf
Reference228 articles.
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2. Adarsha Kumar, K., Ratnam, C., Venkata Rao, K., & Murthy, B. S. N. (2018). Experimental studies of machining parameters on surface roughness, flank wear, cutting forces and work piece vibration in boring of AISI 4340 steels: Modelling and optimization approach. SN Applied Sciences. https://doi.org/10.1007/s42452-018-0026-7
3. Adineh, M., & Doostmohammadi, H. (2021). A hybrid approach based on artificial neural network and cuckoo algorithm for optimization of the main cutting force during turning of Si brass alloys. SN Applied Sciences. https://doi.org/10.1007/s42452-020-04075-1
4. Agarwal, A., & Desai, K. A. (2020). Amalgamation of physics-based cutting force model and machine learning approach for end milling operation. Procedia CIRP, 93, 1405–1410. https://doi.org/10.1016/j.procir.2020.04.102
5. Aghazadeh, F., Tahan, A., & Thomas, M. (2018). Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process. The International Journal of Advanced Manufacturing Technology, 98(9–12), 3217–3227. https://doi.org/10.1007/s00170-018-2420-0
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