A deep neural network for classification of melt-pool images in metal additive manufacturing-Reference-Cited by-同舟云学术

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A deep neural network for classification of melt-pool images in metal additive manufacturing

Published:2018-10-17 Issue:2 Volume:31 Page:375-386
ISSN:0956-5515
Container-title:Journal of Intelligent Manufacturing
language:en
Short-container-title:J Intell Manuf

Author:

Kwon Ohyung,Kim Hyung Giun,Ham Min Ji,Kim Wonrae,Kim Gun-Hee,Cho Jae-Hyung,Kim Nam Il,Kim Kangil

Funder

Korea Institute of Industrial Technology

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Link

http://link.springer.com/content/pdf/10.1007/s10845-018-1451-6.pdf

Reference42 articles.

1. Abhishek, K., Kumar, V. R., Datta, S., & Mahapatra, S. S. (2017). Parametric appraisal and optimization in machining of CFRP composites by using TLBO (Teaching Learning Based Optimization algorithm). Journal of Intelligent Manufacturing,28(8), 1769–1785.

2. Aminzadeh, M., & Kurfess, T. R. (2018). Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-018-1412-0 .

3. Bauereiß, A., Scharowsky, T., & Körner, C. (2014). Defect generation and propagation mechanism during additive manufacturing by selective beam melting. Journal of Materials Processing Technology,214(11), 2522–2528.

4. Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks,5(2), 157–166.

5. Berumen, S., Bechmann, F., Lindner, S., Kruth, J.-P., & Craeghs, T. (2010). Quality control of laser- and powder bed-based Additive Manufacturing (AM) technologies. Physics Procedia,5(13), 617–622.

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