Convolutional neural network for homogenization of particulate composite materials based on finite element data

This study develops a convolutional neural network model to predict the apparent mechanical properties of particulate composite materials based on finite element data. The particulate composite material is considered with random inclusions in size and position. The datasets for training and testing...

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Bibliographic Details
Main Authors: Tien-Thinh Le, Quoc Dat Ha, Huan Thanh Duong
Format: Article
Language:English
Published: Publishing House for Science and Technology 2025-03-01
Series:Vietnam Journal of Mechanics
Subjects:
Online Access:https://vjs.ac.vn/index.php/vjmech/article/view/22319
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Summary:This study develops a convolutional neural network model to predict the apparent mechanical properties of particulate composite materials based on finite element data. The particulate composite material is considered with random inclusions in size and position. The datasets for training and testing processes are generated by using a validated finite element simulation. Various parametric studies are then investigated, including model efficiency and uncertainty propagation. Moreover, the influence of the constituents and microstructure is numerically revealed based on the proposed convolutional neural network model. It is shown that the developed convolutional neural network model is capable of capturing the microstructural features and provides accurate predictions of apparent mechanical properties of particulate composite materials.
ISSN:0866-7136
2815-5882