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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | Virtual and Physical Prototyping |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17452759.2025.2515240 |
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