Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring

Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring

This study presents a self-supervised Bayesian Neural Network (BNN) framework using air-borne Acoustic Emission (AE) to identify different Laser Powder Bed Fusion (LPBF) process regimes such as Lack of Fusion, conduction mode, and keyhole without ground-truth information. The proposed framework addr...

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Detalles Bibliográficos
Main Authors: Vigneashwara Pandiyan, Rafał Wróbel, Roland Axel Richter, Marc Leparoux, Christian Leinenbach, Sergey Shevchik
Formato: Artigo
Idioma:English
Publicado: Elsevier 2023-11-01
Series:Materials & Design
Subjects:
Process monitoring
LPBF
Acoustic emission
Self-supervised CNN
Representation learning
Acceso en liña:http://www.sciencedirect.com/science/article/pii/S0264127523008730

Internet

http://www.sciencedirect.com/science/article/pii/S0264127523008730

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