Classifying Powder Flowability for Cold Spray Additive Manufacturing Using Machine Learning-Reference-Cited by-同舟云学术

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Classifying Powder Flowability for Cold Spray Additive Manufacturing Using Machine Learning

Published:2020-12-10 Issue: Volume: Page:
ISSN:
Container-title:2020 IEEE International Conference on Big Data (Big Data)
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Author:

Valente Richard,Ostapenko Alissa,Sousa Bryer C.,Grubbs Jack,Massar Christopher J.,Cote Danielle L.,Neamtu Rodica

Funder

United States Army

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9377717/9377728/09377948.pdf?arnumber=9377948

Cited by 16 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. Big data, machine learning, and digital twin assisted additive manufacturing: A review;Materials & Design;2024-08

2. On the performance of expert-augmented machine learning with limited experimental data collected from powder particle characteristics used in laser powder bed fusion;The International Journal of Advanced Manufacturing Technology;2024-07-09

3. Critical Velocity for Cold-Sprayed Coatings;Transactions of the Indian Institute of Metals;2024-06-11

4. Recent Advancements in Material Waste Recycling: Conventional, Direct Conversion, and Additive Manufacturing Techniques;Recycling;2024-05-21

5. Cold spray deposition of cermets: insights into bonding mechanism and critical parameters;The International Journal of Advanced Manufacturing Technology;2024-05-16

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