A data-driven machine learning approach for the 3D printing process optimisation-Reference-Cited by-同舟云学术

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A data-driven machine learning approach for the 3D printing process optimisation

Published:2022-05-03 Issue:4 Volume:17 Page:768-786
ISSN:1745-2759
Container-title:Virtual and Physical Prototyping
language:en
Short-container-title:Virtual and Physical Prototyping

Author:

Nguyen Phuong Dong¹²,Nguyen Thanh Q.³,Tao Q. B.⁴,Vogel Frank²,Nguyen-Xuan H.¹^ORCID

Affiliation:

1. CIRTech Institute, HUTECH University, Ho Chi Minh City, Vietnam

2. InuTech Gmbh, Nuernberg, Germany

3. Faculty of Engineering and Technology, Thu Dau Mot University, Binh Duong Province, Vietnam

4. Faculty of Mechanical Engineering, The University of Danang - University of Science and Technology, Danang, Vietnam

Funder

Vingroup Innovation Foundation

Alexander von Humboldt Foundation

European Commission

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Computer Graphics and Computer-Aided Design,Modeling and Simulation,Signal Processing

Link

https://www.tandfonline.com/doi/pdf/10.1080/17452759.2022.2068446

Reference54 articles.

1. Optimization of fused deposition modeling parameters for improved PLA and ABS 3D printed structures

2. Rapid High-Resolution Visible Light 3D Printing

3. Additive Manufacturing of Polymer Materials: Progress, Promise and Challenges

4. 3D Printing with the Commercial UV-Curable Standard Blend Resin: Optimized Process Parameters towards the Fabrication of Tiny Functional Parts

5. Breiman, L. 1997. “Arcing The Edge – Technical Report 486.” Statistics Department, University of California, Berkeley.

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4. Therapeutic Precision: Unveiling the Potential of 3D Printing in Drug Delivery, Tissue Engineering, and Regenerative Medicine;3D Printing and Additive Manufacturing;2024-05-06

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