Predicting Ductile–Brittle transition temperatures for polyolefins using convolutional neural networks and instrumented notched Charpy experiments-Reference-Cited by-同舟云学术

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Predicting Ductile–Brittle transition temperatures for polyolefins using convolutional neural networks and instrumented notched Charpy experiments

Published:2024-03 Issue: Volume:296 Page:126797
ISSN:0032-3861
Container-title:Polymer
language:en
Short-container-title:Polymer

Author:

Kiehas Florian^ORCID,Reiter Martin^ORCID,Torres Juan Pablo^ORCID,Jerabek Michael^ORCID,Major Zoltán

Publisher

Elsevier BV

Reference58 articles.

1. International Organization for Standardization, Plastics - Determination of Charpy Impact Properties - Part 2: Instrumented Impact Testing, Technical Report ISO 179-2:2020, 2020, URL https://www.iso.org/standard/75825.html.

2. A model for toughening of semicrystalline polymers;Corté;Macromolecules,2007

3. High strain rate mechanics of polymers: A review;Siviour;J. Dyn. Behav. Mater.,2016

4. Learning constitutive relations from indirect observations using deep neural networks;Huang;J. Comput. Phys.,2020

5. Mechanical Behavior of Materials;Meyers,2008

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1. Deep learning identifies transversely isotropic material properties using kinematics fields;International Journal of Mechanical Sciences;2024-12

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