AI based monitoring system for DED part quality evaluation-Reference-Cited by-同舟云学术

AI based monitoring system for DED part quality evaluation

Published:2022 Issue: Volume:107 Page:635-640
ISSN:2212-8271
Container-title:Procedia CIRP
language:en
Short-container-title:Procedia CIRP

Author:

Vandone Ambra,Valente Anna

Publisher

Elsevier BV

Subject

General Medicine

Reference18 articles.

1. "An overview of Direct Laser Deposition for additive manufacturing; Part I";Thompson;Additive Manufacturing,2015

2. Optomec, “LENS Software,” 2021. https://optomec.com/3d-printed-metals/lens-software/#four

3. DMG Mori, 2021. https://ch-de.dmgmori.com/produkte/maschinen/additive-manufacturing/pulverdueseverfahren

4. Song L. and Mazumder J., “Real-time implementation of generalized predictive algorithm for direct metal deposition (DMD) process control”, US 9,044,827 B2, 2015.

5. Adaxis, https://adaxis.eu/the-platform/

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Characterization of laser cladding functional coatings: an in-situ monitoring and variability analysis approach;The International Journal of Advanced Manufacturing Technology;2024-05-28

2. Height consistency compensation in laser-directed energy deposition of thin-walled parts;International Journal of Mechanical Sciences;2024-03

3. Machine learning in Directed Energy Deposition (DED) additive manufacturing: A state-of-the-art review;Manufacturing Letters;2023-08

4. Online 3D Geometry Reconstruction for Direct Energy Deposition Based on Melt Pool Images;2023 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT);2023-06-06