Open Access
MATEC Web Conf.
Volume 192, 2018
The 4th International Conference on Engineering, Applied Sciences and Technology (ICEAST 2018) “Exploring Innovative Solutions for Smart Society”
Article Number 01044
Number of page(s) 4
Section Track 1: Industrial Engineering, Materials and Manufacturing
Published online 14 August 2018
  1. V. Karri, and T. Kiatcharoenpol, A monitoring system of drill wear states using a hybrid neural network, Materials Science Forum (2004) [Google Scholar]
  2. V. Karri, and T. Kiatcharoenpol, Tool condition monitoring in drilling using artificial neural networks, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2003) [Google Scholar]
  3. V. Karri, and T. Kiatcharoenpol, Prediction of Internal Surface Roughness in Drilling Using Three Feedforward Neural Networks - A Comparison, 9th International Conference On Neural Information Processing, Singapore, (2002) [Google Scholar]
  4. C. Montgomery Douglas, Design and analysis of experiments. ed. Hoboken, NJ : Wiley, (2013) [Google Scholar]
  5. F. Ortega-Zamorano, J. M. Jerez, D. Urda Muñoz, R. M. Luque-Baena, L. Franco, Efficient Implementation of the Backpropagation Algorithm in FPGAs and Microcontrollers IEEE Transactions on Neural Networks and Learning Systems (2016) [Google Scholar]
  6. N. V. Irukulapati, D. Marsella, P. Johannisson, E. Agrell, M. Secondini, H. Wymeersch, Stochastic Digital Backpropagation With Residual Memory Compensation, Journal of Lightwave Technology (2016) [Google Scholar]

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