Enhancing defect detection in additive manufacturing using a conditional autoencoder with skip connections and in situ infrared sensing

Developing reliable and efficient defect detection strategies for additive manufacturing (AM) is critical to advancing its adoption in industrial environments. To this end, studies have focused on constructing defect detection frameworks combining unsupervised anomaly detection algorithms and sensor...

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Bibliographic Details

Published in:	Journal of manufacturing processes Vol. 156; pp. 268 - 283
Main Authors:	Segura Ibarra, Diego, Hossain, Rifat-E-Nur, Lamonte, Riley C., Moore, Arden L., Chen, Jinyuan
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 26.12.2025
Subjects:	Anomaly detection Deep learning Fused deposition modeling Melt-pool temperature sensing Process monitoring Self-supervised learning Process monitoring Deep learning Melt-pool temperature sensing Fused deposition modeling Anomaly detection Self-supervised learning
ISSN:	1526-6125
Online Access:	Get full text
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