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Research Article

Research on tool wear prediction based on the random forest optimized by NGO algorithm

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References

  • Cheng, Y.N.; Lu, M.D.; Gai, X.Y.; Guan, R.; Zhou, S.L.; Xue, J. (2024) Research on multi-signal milling tool wear prediction method based on GAF-ResNext. Robotics and Computer-Integrated Manufacturing, 85: 102634. doi:10.1016/j.rcim.2023.102634
  • Colantonio, L.; Equeter, L.; Dehombreux, P.; Ducobu, F. (2021) A systematic literature review of cutting tool wear monitoring in turning by using artificial intelligence techniques. Machines, 9(12): 351. doi:10.3390/machines9120351
  • Das, A.; Das, S.R.; Panda, J.P.; Dey, A.; Gajrani, K.K.; Somani, N.; Gupta, N.K. (2022) Machine learning-based modelling and optimization in hard turning of AISI D6 steel with advanced AlTiSiN-COATED carbide inserts to predict surface roughness and other machining characteristics. Surface Review and Letters, 29(10): 2250137. doi:10.1142/S0218625X22501372
  • Dehghani, M.; Hubalovsky, S.; Trojovsky, P. (2021) Northern goshawk optimization: A new swarm-based algorithm for solving optimization problems. IEEE Access.9: 162059–162080. 10.1109/ACCESS.2021.3133286
  • Dong, D.L.; Wang, T.Z.; Wang, J.H.; Niu, J.T.; Qiao, Y.; Wang, X.Y. (2023) Study on tool wear state monitoring based on EEMD information entropy and PSO-SVM. Journal of Physics: Conference Series, 2566(1): 012111. doi: 10.1088/1742-6596/2566/1/012111
  • Gai, X.Y.; Cheng, Y.N.; Guan, R.; Jin, Y.B.; Lu, M.D. (2022) Tool wear state recognition based on WOA-SVM with statistical feature fusion of multi-signal singularity. The International Journal of Advanced Manufacturing Technology, 123(7-8): 2209–2225. doi:10.1007/s00170-022-10342-9
  • Ge, Y.S.; Zhang, J.H.; Song, G.H.; Zhu, K.Y. (2023) An effective LSSVM-based approach for milling tool wear prediction. The International Journal of Advanced Manufacturing Technology, 126(9-10): 4555–4571. doi:10.1007/s00170-023-11421-1
  • Han, D.Y.; Yu, J.S.; Tang, D.Y. (2021) An HDP-HMM based approach for tool wear estimation and tool life prediction. Quality Engineering, 33(2): 208–220. doi:10.1080/08982112.2020.1813760
  • Jehad, A.; Rehanullah, K.; Nasir, A.; Imran, M. (2012) Random forests and decision trees. International Journal of Computer Science Issues, 9(5): 272–278.
  • Kong, D.D.; Chen, Y.J.; Li, N. (2020) Monitoring tool wear using wavelet package decomposition and a novel gravitational search algorithm-least square support vector machine model. Journal of Mechanical Engineering Science, 234(3): 822–836. doi:10.1177/0954406219887318
  • Li, H.K.; Zhou, S.; Sun, Z.H. (2009) Investigation on machine conditions classification by using hilbert spectrum feature extraction and support vecyor machine. Journal of Vibration and Shock, 28: 131–136. doi:10.13465/j.cnki.jvs.2009.06.022
  • Li, X.; Zhang, Y.; Zhu, K.P. (2022) Tool wear monitoring method based on s-transform time-frequency characteristics. Modular Machine Tool & Automatic Manufacturing Technique, 10: 88–96. doi:10.13462/j.cnki.mmtamt.2022.10.019
  • Li, X.W.; Qin, X.J.; Wu, J.X.; Yang, J.F.; Huang, Z. (2022) Tool wear prediction based on convolutional bidirectional LSTM model with improved particle swarm optimization. The International Journal of Advanced Manufacturing Technology, 123(11-12): 4025–4039. doi:10.1007/s00170-022-10455-1
  • Li, Y.X.; Huang, X.Z.; Tang, J.W.; Li, S.J.; Ding, P.F. (2023) A steps-ahead tool wear prediction method based on support vector regression and particle filtering. Measurement, 218: 113237. doi:10.1016/j.measurement.2023.113237
  • Ma, W.; Liu, X.L.; Yue, C.X.; Wang, L.H.; Liang, S.Y. (2023) Multi-scale one-dimensional convolution tool wear monitoring based on multi-model fusion learning skills. Journal of Manufacturing Systems, 70: 69–98. doi:10.1016/j.jmsy.2023.07.007
  • Ma, Z.P.; Zhao, M.; Dai, X.B.; Chen, Y. (2023) A hybrid-driven probabilistic state space model for tool wear monitoring. Mechanical Systems and Signal Processing, 200: 110599. doi:10.1016/j.ymssp.2023.110599
  • Meng, X.F.; Zhang, J.J.; Xiao, G.C.; Chen, Z.Q.; Yi, M.D.; Xu, C.H. (2021) Tool wear prediction in milling based on a GSA-BP model with a multisensor fusion method. The International Journal of Advanced Manufacturing Technology, 114(11-12): 3793–3802. doi:10.1007/s00170-021-07152-w
  • PHM Society (2010) PHM society conference data challenge. https://www.phmsociety.org/competition/phm/10.
  • Rao, K.V.; Kumar, Y.P.; Sing, D.V.; Raju, L.S.; Jinka, R. (2021) Vibration based tool condition monitoring in milling of Ti-6Al-4V using an optimization model of GM(1, N) and SVM. The International Journal of Advanced Manufacturing Technology, 115(5-6): 1931–1941. doi:10.21203/rs.3.rs-285124/v1 10.1007/s00170-021-07280-3
  • Song, G.H.; Zhang, J.H.; Zhu, K.Y.; Ge, Y.S.; Yu, L.C.; Fu, Z.S. (2023) Tool wear monitoring based on multi-kernel gaussian process regression and stacked multilayer denoising autoencoders. Mechanical Systems and Signal Processing, 186: 109851. doi:10.1016/j.ymssp.2022.109851
  • Twardowski, P.; Czyżycki, J.; Felusiak-Czyryca, A.; Tabaszewski, M.; Wiciak-Pikuła, M. (2023) Monitoring and forecasting of tool wear based on measurements of vibration accelerations during cast iron milling. Journal of Manufacturing Processes, 95: 342–350. doi:10.1016/j.jmapro.2023.04.036
  • Wang, S.; Gong, J.; Yang, X.M. (2022) An incipient cable failures identification method based on S-transform combined with mrmr feature selection. Computer Applications and Software, 39: 206–211. doi:10.3969/j.issn.1000-386x.2022.01.031
  • Wang, S.Q.; Yan, S.C.; Sun, Y.W. (2023) Milling tool condition monitoring for difficult-to-cut materials based on NCAE and IGWO-SVM. The International Journal of Advanced Manufacturing Technology, 129(3-4): 1355–1374. doi:10.1007/S00170-023-12313-0
  • Wei, W.H.; Cong, R.; Li, Y.T.; Abraham, A.D.; Yang, C.Y.; Chen, Z.T. (2022) Prediction of tool wear based on GA-BP neural network. Proceedings of the Institution of Mechanical Engineers, 236(12): 1564–1573. doi:10.1177/09544054221078144
  • Wu, D.Z.; Jennings, C.; Terpenny, J.; Gao, R.X.; Kumara, S. (2017) A comparative study on machine learning algorithms for smart manufacturing: tool wear prediction using random forests. Journal of Manufacturing Science and Engineering, 139(7): 071018. doi:10.1115/1.4036350
  • Zhou, W.J.; Xiao, X.P.; Li, Z.S.; Zhang, K.; He, R.D. (2024) Prediction tool wear using improved deep extreme learning machines based on the sparrow search algorithm. Measurement Science and Technology, 35(4): 046112. doi:10.1088/1361-6501/AD1BA0
  • Zhu, K.P.; Liu, T.S. (2018) Online tool wear monitoring via hidden semi-markov model with dependent durations. IEEE Transactions on Industrial Informatics, 14(1): 69–78. doi:10.1109/tii.2017.2723943
  • Zhu, Z.Y.; Liu, R.L.; Zeng, Y.F. (2023) Tool wear condition monitoring based on multi-sensor integration and deep residual convolution network. Engineering Research Express, 5(1): 015054. doi:10.1088/2631-8695/acbfa6

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