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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Vydané v:	Journal of manufacturing processes Ročník 156; s. 268 - 283
Hlavní autori:	Segura Ibarra, Diego, Hossain, Rifat-E-Nur, Lamonte, Riley C., Moore, Arden L., Chen, Jinyuan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 26.12.2025
Predmet:	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
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