Detecting the extent of co-existing anomalies in additively manufactured metal matrix composites through explainable selection and fusion of multi-camera deep learning features

Process development for customised additively manufactured materials is challenging and labour-intensive. Advanced in-situ monitoring coupled with modern machine learning (ML) methods can expedite defect detection and qualification of additive manufacturing (AM) parts. Directed energy deposition (DE...

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Hauptverfasser:	Mutahar Safdar, Gentry Wood, Max Zimmermann, Guy Lamouche, Priti Wanjara, Yaoyao Fiona Zhao
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Taylor & Francis Group 2025-12-01
Schriftenreihe:	Virtual and Physical Prototyping
Schlagworte:	Defect extent detection co-existing anomalies metal matrix composites explainable AI video transformer features
Online-Zugang:	https://www.tandfonline.com/doi/10.1080/17452759.2025.2515240
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