Product quality prediction in pulsed laser cutting of silicon steel sheet using vibration signals and deep neural network
Author:
Funder
national science council
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Link
https://link.springer.com/content/pdf/10.1007/s10845-021-01881-1.pdf
Reference41 articles.
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2. Anicic, O., Jović, S., Skrijelj, H., & Nedić, B. (2017). Prediction of laser cutting heat affected zone by extreme learning machine. Optics Lasers in Engineering, 88, 1–4. https://doi.org/10.1016/j.optlaseng.2016.07.005
3. Baek, J., & Choi, Y. (2019). Deep neural network for ore production and crusher utilization prediction of truck haulage system in underground mine. Applied Sciences, 9(19), 4180. https://doi.org/10.3390/app9194180
4. Belhadj, A., Baudouin, P., & Houbaert, Y. (2002). Simulation of the HAZ and magnetic properties of laser-cut non-oriented electrical steels. Journal of Magnetism and Magnetic Materials, 248, 34–44. https://doi.org/10.1016/S0304-8853(02)00064-1
5. Da Silva, P. C. L., De Melo, R. R., & Da Silva, J. P. (2020). Optical fiber coupler analysis using daubechies wavelets. Journal of Microwaves, Optoelectronics and Electromagnetic Applications, 19, 294–300. https://doi.org/10.1590/2179-10742020v19i3825
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