Optimization of Silicone 3D Printing with Hierarchical Machine Learning-Reference-Cited by-同舟云学术

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Optimization of Silicone 3D Printing with Hierarchical Machine Learning

Published:2019-08 Issue:4 Volume:6 Page:181-189
ISSN:2329-7662
Container-title:3D Printing and Additive Manufacturing
language:en
Short-container-title:3D Printing and Additive Manufacturing

Author:

Menon Aditya¹,Póczos Barnabás²,Feinberg Adam W.¹³,Washburn Newell R.⁴

Affiliation:

1. Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania.

2. Department of Machine Learning, Carnegie Mellon University, Gates-Hillman Center, Pittsburgh, Pennsylvania.

3. Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania.

4. Department of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania.

Publisher

Mary Ann Liebert Inc

Subject

Industrial and Manufacturing Engineering,Materials Science (miscellaneous)

Link

https://www.liebertpub.com/doi/pdf/10.1089/3dp.2018.0088

Reference30 articles.

1. Printing soft matter in three dimensions

2. Manufacturing Ti-6Al-4V Components by Shaped Metal Deposition: Microstructure and Mechanical Properties

3. Mechanical properties of dense polylactic acid structures fabricated by three dimensional printing

4. Recent advances in 3D printing of biomaterials

5. 3D bioprinting of tissues and organs

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2. Polymer material framework for 3D printing using fusion deposition modelling;Progress in Additive Manufacturing;2024-09-11

3. Multi-Image Fusion-Based Defect Detection Method for Real-Time Monitoring of Recoating in Ceramic Additive Manufacturing;3D Printing and Additive Manufacturing;2024-08-29

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5. Optimization of 4D/3D printing via machine learning: A systematic review;Hybrid Advances;2024-08

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