Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning

Differentiation of materials and laser powder bed fusion processing regimes from airborne acoustic emission combined with machine learning

This study investigates the use of a low cost microphone combined with state-of-the-art machine learning (ML) algorithms as online process monitoring to differentiate various materials and process regimes of Laser-Powder Bed Fusion (LPBF). Three processing regimes (lack of fusion pores, conduction m...

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Bibliographic Details
Main Authors: Rita Drissi-Daoudi, Vigneashwara Pandiyan, Roland Logé, Sergey Shevchik, Giulio Masinelli, Hossein Ghasemi-Tabasi, Annapaola Parrilli, Kilian Wasmer
Format: Article
Language:English
Published: Taylor & Francis Group 2022-04-01
Series:Virtual and Physical Prototyping
Subjects:
acoustic emission
additive manufacturing
in situ monitoring
machine learning
artificial intelligence
laser material processing
Online Access:http://dx.doi.org/10.1080/17452759.2022.2028380

Internet

http://dx.doi.org/10.1080/17452759.2022.2028380

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