Deep semi-supervised learning of dynamics for anomaly detection in laser powder bed fusion
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DOI: 10.1007/s10845-021-01842-8
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- Han, Zhezhe & Hossain, Md. Moinul & Wang, Yuwei & Li, Jian & Xu, Chuanlong, 2020. "Combustion stability monitoring through flame imaging and stacked sparse autoencoder based deep neural network," Applied Energy, Elsevier, vol. 259(C).
- Attaran, Mohsen, 2017. "The rise of 3-D printing: The advantages of additive manufacturing over traditional manufacturing," Business Horizons, Elsevier, vol. 60(5), pages 677-688.
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- Jiyoung Song & Young Chul Lee & Jeongsu Lee, 2023. "Deep generative model with time series-image encoding for manufacturing fault detection in die casting process," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3001-3014, October.
- Aniruddha Gaikwad & Tammy Chang & Brian Giera & Nicholas Watkins & Saptarshi Mukherjee & Andrew Pascall & David Stobbe & Prahalada Rao, 2022. "In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2093-2117, October.
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Keywords
Additive manufacturing (AM); Anomaly detection; Deep learning; Dynamics; Laser powder bed fusion (L-PBF); State space models (SSM);All these keywords.
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