Prediction of Cutting Forces for Machine Tools by Neural Networks
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-56463-5_5
Reference13 articles.
1. Amigo, F.J., et al.: Prediction of cutting forces including tool wear in high-feed turning of Nimonic ® C-263 superalloy: a geometric distortion-based model. Measurement 211, 112–580 (2023). https://doi.org/10.1016/j.measurement.2023.112580
2. Paturi, U.M.R., Cheruku, S., Pasunuri, V.P.K., Salike, S.: Modeling of tool wear in machining of AISI 52100 steel using artificial neural networks. Mater Today Proc. 3, 234–465 (2020). https://doi.org/10.1016/j.matpr.2020.06.581
3. Olalere, I.O., Olanrewaju, O.A.: Optimising turning operation in precision manufacturing using fused IoT devices and machine learning approach. IFAC Pap. 55, 1551–1555 (2022). https://doi.org/10.1016/j.ifacol.2022.09.611
4. Sankar, M.R., et al.: Experimental study and artificial neural network modeling of machining with minimum quantity cutting fluid. Mater. Today Proc. 18, 4921–4931 (2019). https://doi.org/10.1016/j.matpr.2019.07.484
5. Peng, B., et al.: A hybrid approach using machine learning to predict the cutting forces under consideration of the tool wear. Procedia CIRP 82, 302–307 (2019). https://doi.org/10.1016/j.procir.2019.04.031
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