Multisensor fusion-based digital twin for localized quality prediction in robotic laser-directed energy deposition

Multisensor fusion-based digital twin for localized quality prediction in robotic laser-directed energy deposition

Early detection of defects, such as keyhole pores and cracks is crucial in laser-directed energy deposition (L-DED) additive manufacturing (AM) to prevent build failures. However, the complex melt pool behaviour cannot be adequately captured by conventional single-modal process monitoring approaches...

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Bibliographic Details
Main Authors: Chen, Lequn, Bi, Guijun, Yao, Xiling, Tan, Chaolin, Su, Jinlong, Ng, Nicholas Poh Huat, Chew, Youxiang, Liu, Kui, Moon, Seung Ki
Other Authors: School of Mechanical and Aerospace Engineering
Format: Journal Article
Language:English
Published: 2023
Subjects:
Engineering::Mechanical engineering
Additive Manufacturing
Multisensor Fusion
Online Access:https://hdl.handle.net/10356/172582

Internet

https://hdl.handle.net/10356/172582

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