Machine learning prediction of the mechanical properties of injection-molded polypropylene through X-ray diffraction analysis

Machine learning prediction of the mechanical properties of injection-molded polypropylene through X-ray diffraction analysis

Predicting the mechanical properties of polymer materials using machine learning is essential for the design of next-generation of polymers. However, the strong relationship between the higher-order structure of polymers and their mechanical properties hinders the mechanical property predictions bas...

Full description

Saved in:
Bibliographic Details
Main Authors: Ryo Tamura, Kenji Nagata, Keitaro Sodeyama, Kensaku Nakamura, Toshiki Tokuhira, Satoshi Shibata, Kazuki Hammura, Hiroki Sugisawa, Masaya Kawamura, Teruki Tsurimoto, Masanobu Naito, Masahiko Demura, Takashi Nakanishi
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:Science and Technology of Advanced Materials
Subjects:
Polypropylene
X-ray diffraction
bayesian spectral deconvolution
ising machine
machine learning
Online Access:https://www.tandfonline.com/doi/10.1080/14686996.2024.2388016
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.tandfonline.com/doi/10.1080/14686996.2024.2388016

Similar Items