Industry 4.1. Группа авторов

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СКАЧАТЬ C.A. and Nichols, J.S. (2009). A low cost wireless high bandwidth transmitter for sensor‐integrated metal cutting tools and process monitoring. International Journal of Mechatronics and Manufacturing Systems 2 (4): 441–454. https://doi.org/10.1504/IJMMS.2009.027128.

      3 3Ghosh, N., Ravi, Y.B., Patra, A. et al. (2007). Estimation of tool wear during CNC milling using neural network‐based sensor fusion. Mechanical Systems and Signal Processing 21 (1): 466–479. https://doi.org/10.1016/j.ymssp.2005.10.010.

      4 4Abuthakeer, S.S., Mohanram, P.V., and Kumar, G.M. (2011). Prediction and control of cutting tool vibration CNC lathe with ANOVA and ANN. International Journal of Lean Thinking 2 (1): 1–23.

      5 5Tieng, H., Li, Y.Y., Tseng, K.P. et al. (2020). An Automated Dynamic‐Balancing‐Inspection Scheme for Wheel Machining. IEEE Robotics and Automation Letters 5 (4): 2224–2231. https://doi.org/10.1109/LRA.2020.2970953.

      6 6Abellan‐Nebot, J.V. and Subirón, F.R. (2010). A review of machining monitoring systems based on artificial intelligence process models. The International Journal of Advanced Manufacturing Technology 47 (14): 237–257. https://doi.org/10.1007/s00170‐009‐2191‐8.

      7 7Teti, R., Jemielniak, K., O’Donnell, G. et al. (2010). Advanced monitoring of machining operations. CIRP Annals 59 (2): 717–739. https://doi.org/10.1016/j.cirp.2010.05.010.

      8 8Yang, H.C., Tieng, H., and Cheng, F.T. (2015). Total precision inspection of machine tools with virtual metrology. Journal of the Chinese Institute of Engineers 39 (2): 221–235. https://doi.org/10.1109/CoASE.2015.7294301.

      9 9Mallat, S.G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11 (7): 674–693. https://doi.org/10.1109/34.192463.

      10 10Zhang, Z., Wang, Y., and Wang, K. (2013). Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network. Journal of Intelligent Manufacturing 24: 1213–1227. https://doi.org/10.1007/s10845‐012‐0657‐2.

      11 11Kim, J., Lee, H., Jeon, J.W. et al. (2020). Stacked auto‐encoder based CNC tool diagnosis using discrete wavelet transform feature extraction. Processes 8 (4): 456. https://doi.org/10.3390/pr8040456.

      12 12Jiang, G., He, H., Xie, P. et al. (2017). Stacked multilevel‐denoising autoencoders: a new representation learning approach for wind turbine gearbox fault diagnosis. IEEE Transactions on Instrumentation and Measurement 66 (9): 2391–2402. https://doi.org/10.1109/TIM.2017.2698738.

      13 13Lee, R.J. and Nicewander, W.A. (1988). Thirteen ways to look at the correlation coefficient. The American Statistician 42 (1): 59–66. https://doi.org/10.1080/00031305.1988.10475524.

      14 14Strang, G. (1994). Wavelets. American Scientist 82 (3): 250–255.

      15 15Belinsky, A.V.E. and Lapshin, V.B. (2017). The uncertainty principle and measurement accuracy. Physics‐Uspekhi 60 (3): 325–326. https://doi.org/10.3367/UFNe.2017.02.038069.

      16 16Yen, G.G. and Lin, K.C. (2000). Wavelet packet feature extraction for vibration monitoring. IEEE Transactions on Industrial Electronics 47 (3): 650–667. https://ieeexplore.ieee.org/document/847906.

      17 17Yang, H.C. (2020). Strain data collected from machine tool. https://ieee‐dataport.org/documents/strain‐data‐collected‐machine‐tool (accessed 26 October 2020).

      18 18Yang, H.C. (2020). Signal segmentation for milling process. https://ieee‐dataport.org/open‐access/signal‐segmentation‐milling‐process (accessed 6 November 2020).

      19 19Yang, H.C. (2020). Roughness of milling process. https://ieee‐dataport.org/open‐access/roughness‐milling‐process (accessed 6 November 2020).

      20 20Yang, H.C. (2020). Cold forging process failures. https://ieee‐dataport.org/open‐access/cold‐forging‐process‐failures (accessed 5 November 2020).

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