Journal of the Korean Society of Manufacturing Technology Engineers (한국생산제조학회지)

The Korean Society of Manufacturing Technology Engineers (한국생산제조학회)
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Simulating Cutting Forces in Milling Machines Using Multi-layered Neural Networks

다층 신경회로망에 의한 밀링가공의 절삭력 시뮬레이션

  • Lee, Sin-Young (School of Mechanical & Automotive Engineering, Kunsan National University)
  • Received : 2016.06.09
  • Accepted : 2016.08.14
  • Published : 2016.08.15
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Abstract

Predicting cutting forces in machine tools is essential to productivity improvement and process control in the manufacturing field. Furthermore, milling machining is more complicated than turning machining. Therefore, several studies have been conducted previously to simulate milling forces; this study aims to simulate the cutting forces in milling machines using multi-layered neural networks. In the experiments, the number of layers in these networks was 3 and 4 and the number of neurons in the hidden layers was varied from 20 to 200. The root mean square errors of simulated cutting force components were obtained from taught and untaught data for the various neural networks. Results show that the error trends for untaught data were non-uniform because of the complex nature of the cutting force components, which was caused by different cutting factors and nonlinear characteristics coming into play. However, trends for taught data showed a very good coincidence.

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References

  1. Lee, S.Y., Im, Y. M., 2004, Prediction and Experiments of Cutting Forces in End Milling, Trans. KSMTE, 13:4 9-15.
  2. Smith, S., Tlusty, J., 1993, Efficient Simulation Programs for Chatter in Milling, Int. J. Mach. Tool Des. Res., 35:7 939-950.
  3. Kline, W. A., DeVor, R. E., Lindberg, J. R., 1982, The Prediction of Cutting Forces in End Milling with Application to Cornering Cuts, Int. J. Mach. Tool Des. Res., 22:11 7-22. https://doi.org/10.1016/0020-7357(82)90016-6
  4. An, H. S., 2016, A Study on Split Training and Merging Method for Effective Initialization of Deep Neural Network, A Thesis for a Doctorate, Inha University, Republic of Korea.
  5. Rumelhart, D. E., Hinton, G. E., Williams, R. J., 1986, Learning Representations by Back Propagation Errors, Nature, 323 533-536. https://doi.org/10.1038/323533a0
  6. Shiotsuka, T., Ohta, K., Yoshida, K., Nagamatsu, A., 1993, Identification and Control of Four-wheel-steering Car by Neural Network, Trans. JSME C, 59 708-713.
  7. Cha, Y. H., Kim, I. S., Kim, H. S., Lee, Y. S., Kim, D. J., Sung, B. S., Suh, J. Y., 2000, A Study on the Prediction of Welded Residual Stresses Using Neural Network, Trans. KSMTE, 9:6 89-95.
  8. Hong, S. H., Cho, S. S., Joo, W. S., 2001, A Study on Fatigue Damage Model Using Neural Network, Trans. KSMTE, 10:4 14-21.
  9. Fukuda, T., Kurihara, T., Tokita, M., Mitsuoka, T., 1990, Position and Force Hybrid Control of Robotic Manipulator by Neural Network, Trans. JSME C, 56 1854-1860. https://doi.org/10.1299/kikaic.56.1854
  10. Lee, S. Y., 2003, Vibration Prediction in Milling Process by Using Neural Network, Trans. KSMTE, 12:5 1-7.