Defect Monitoring of Complex Geometries Through Machine Learning in LPBF Metal Additive Manufacturing
Laser powder bed fusion (LPBF) can fabricate intricate metal components but is prone to defects, such as porosity and cracks, that degrade performance. We present an in situ monitoring framework that fuses structure-borne acoustic emission (AE) and coaxial two-color pyrometry acquired synchronously...
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| Principais autores: | , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
MDPI AG
2026-04-01
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| Series: | Journal of Manufacturing and Materials Processing |
| Assuntos: | |
| Acceso en liña: | https://www.mdpi.com/2504-4494/10/4/127 |
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