Acknowledgement
This work was supported by Incheon National University Research Grant in 2020.
References
- Shahid, M. B., Han. S.-C., Jun, T. S., Park, D. S., "Effect of process parameters on the joint strength in ultrasonic welding of Cu and Ni foils," Materials Manufacturing Processes, Vol. 34, pp. 1217-1224, 2019. https://doi.org/10.1080/10426914.2019.1643474
- Shahid, M. B., Jung, J. Y., Park, D. S., "Finite element analysis coupled artificial neural network approach to design the longitudinal-torsional mode ultrasonic welding horn," International Journal of Advanced Manufacturing Technology, Vol. 107, pp. 2731-2743, 2020. https://doi.org/10.1007/s00170-020-05200-5
- Lin, S., "Sandwiched piezoelectric ultrasonic transducers of longitudinal-torsional compound vibrational modes," IEEE Transaction on Ultrasonics, Ferroelectrics, and Frequency Control, Vol. 44, No. 6, pp. 1189-1197, 1997. https://doi.org/10.1109/58.656619
- Tsujino, J., Ueoka, T., Hasegawa, K., et al., "New Methods of Ultrasonic Welding of Metal and Plastic Materials," Ultrasonics, Vol. 34, pp. 177-185, 1996. https://doi.org/10.1016/0041-624X(96)81780-X
- Tsujino, J., "Recent Developments of Ultrasonic Welding," Proc. of 1995 IEEE Ultrasonics Symposium, pp. 1051-1060, 1995.
- Lee, S. S., Kim, T. H., Hu, S. J., Cai, W. W., Abell, J. A., Li, J., "Characterization of Joint Quality in Ultrasonic Welding of Battery Tabs," Journal of Manufacturing Science and Engineering, Vol. 135, No. 2, pp. 021004, 2013. https://doi.org/10.1115/1.4023364
- Chang, U. I., Frisch, J., "On optimization of some parameters in ultrasonic metal welding," Welding Journal, Vol. 1, pp. 24-35, 1974.
- Wan, X., Wang, Y., Zhao, D., Huang, Y., Yin, Z., "Weld quality monitoring research in small scale resistance spot welding by dynamic resistance and neural network," Measurement, Vol. 99, pp. 120-127, 2017. https://doi.org/10.1016/j.measurement.2016.12.010
- Lei, Z., Shen, J., Wang, Q., Chen, Y.,"Real-time weld geometry prediction based on multi- information using neural network optimized by PCA and GA during thin-plate laser welding," Journal of Manufacturing Processes, Vol. 43, pp. 207-217, 2019. https://doi.org/10.1016/j.jmapro.2019.05.013
- Nwankpa, C. E., Ijomah, W. I., Gachagan, A., Marshall, S., "Activation Functions: Comparison of Trends in Practice and Research for Deep Learning," 2nd International Conf. on INCCST, Jamshoro, Pakistan, 2020.
- Han, J., Moraga, C., "The influence of the sigmoid function parameters on the speed of backpropagation learning," Lecture Notes in Computer Science, Springer, pp. 195-201, 1995.
- Neal, R. M., "Connectionist learning of belief networks," Artificial Intelligence, Vol. 56, pp. 71-113, 1992. https://doi.org/10.1016/0004-3702(92)90065-6
- LeCun, Y., Bengio, Y., Hinton, G., "Deep learning," Nature, Vol. 521, pp. 436-444, 2015. https://doi.org/10.1038/nature14539
- Karlik, B., Olgac, A. V., "Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks," International Journal of Artificial Intelligence And Expert Systems(IJAE), Vol. 1, Issue 4, pp. 111-122, 2011.
- Nair, V., Hinton, G. E., "Rectified Linear Units Improve Restricted Boltzmann Machines," Proc. of the 27th International Conference on Machine Learning, Israel, pp. 807-814, 2010.
- Pedregosa, F., Varoquaux, G., Gramfort, A., et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, Vol. 12, pp. 2825-2830, 2011.