Probabilistic physics-guided transfer learning for material property prediction in extrusion deposition additive manufacturing-Reference-Cited by-同舟云学术

Probabilistic physics-guided transfer learning for material property prediction in extrusion deposition additive manufacturing

Published:2024-02 Issue: Volume:419 Page:116660
ISSN:0045-7825
Container-title:Computer Methods in Applied Mechanics and Engineering
language:en
Short-container-title:Computer Methods in Applied Mechanics and Engineering

Author:

Thomas Akshay J.^ORCID,Jaszczuk Mateusz,Barocio Eduardo,Ghosh Gourab^ORCID,Bilionis Ilias,Pipes R. Byron

Publisher

Elsevier BV

Subject

Computer Science Applications,General Physics and Astronomy,Mechanical Engineering,Mechanics of Materials,Computational Mechanics

Reference59 articles.

1. E. Barocio, B. Brenken, A. Favaloro, R. Pipes, Extrusion deposition additive manufacturing of composite molds for high-temperature applications, in: Proceedings of the Int. SAMPE Tech. Conf., Seattle, WA, USA, 2017, pp. 22–25.

2. A.A. Hassen, R. Springfield, J. Lindahl, B. Post, L. Love, C. Duty, U. Vaidya, R.B. Pipes, V. Kunc, The durability of large-scale additive manufacturing composite molds, in: CAMX 2016, 2016, pp. 26–29.

3. The importance of carbon fiber to polymer additive manufacturing;Love;J. Mater. Res.,2014

4. Fusion Bonding of Fiber Reinforced Semi-Crystalline Polymers in Extrusion Deposition Additive Manufacturing;Barocio,2020

5. Development and validation of extrusion deposition additive manufacturing process simulations;Brenken;Addit. Manuf.,2019

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