Machine learning for continuous liquid interface production: Printing speed modelling-Reference-Cited by-同舟云学术

Machine learning for continuous liquid interface production: Printing speed modelling

Published:2019-01 Issue: Volume:50 Page:236-246
ISSN:0278-6125
Container-title:Journal of Manufacturing Systems
language:en
Short-container-title:Journal of Manufacturing Systems

Author:

He Haiyang,Yang Yang,Pan Yayue

Funder

National Science Foundation

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Hardware and Architecture,Software,Control and Systems Engineering

Reference23 articles.

1. Continuous liquid interface production of 3D objects;Tumbleston;Science,2015

2. Chen, Y., Mao, H. and Li, X., University of Southern California (USC), 2016. Mask Video Projection Based Stereolithography with Continuous Resin Flow. U.S. Patent Application 15/187,713.

3. Recent advances and trends in predictive manufacturing systems in big data environment;Lee;Manuf Lett,2013

4. Overview of additive manufacturing informatics:“a digital thread”;Mies;Integr Mater Manuf Innov,2016

5. Additive manufacturing and big data;Wang;Int J Math Eng Manag Sci,2016

Cited by 57 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. 3D printing of biodegradable polymers and their composites – Current state-of-the-art, properties, applications, and machine learning for potential future applications;Progress in Materials Science;2024-12

2. A review on recent advancements in additive manufacturing techniques;Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering;2024-08-30

3. Productivity enhancement in top-down VPP via concurrent grayscaling and platform speed profile optimization for symmetrical parts having micro scale features;Progress in Additive Manufacturing;2024-06-14

4. Machine learning assisted material development for lithography-based additive manufacturing of porous alumina ceramics;Open Ceramics;2024-06

5. Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing;Advanced Materials;2024-03-28