A deep learning based prediction model for effective elastic properties of porous materials
Abstract With the development of the economy and society, porous materials have been widely used in various fields due to their unique structure and function. Therefore, it is of great significance to analyze the mechanical properties of porous materials. In traditional analysis methods, experimenta...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-90016-0 |
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| author | Chang Liu Ran Guo Yangming Su |
| author_facet | Chang Liu Ran Guo Yangming Su |
| author_sort | Chang Liu |
| collection | DOAJ |
| description | Abstract With the development of the economy and society, porous materials have been widely used in various fields due to their unique structure and function. Therefore, it is of great significance to analyze the mechanical properties of porous materials. In traditional analysis methods, experimental and numerical simulation methods are mainly used. When conducting finite element numerical simulation analysis on porous materials, a large number of fine grids need to be divided, and the calculation process is time-consuming and laborious. This article randomly generates porous microstructure models through algorithms and uses efficient quadtree algorithms to calculate their mechanical properties, thereby obtaining a large amount of machine-learning sample data. Furthermore, a neural network-based machine learning algorithm is established to predict the mechanical properties of porous materials. By using microstructure images as the input layer of the model, the mechanical properties under corresponding conditions can be directly predicted. This study provides a new method for predicting mechanical properties based on microstructure images. It has been verified that the mechanical properties directly predicted by the network are similar to the actual ones, with high accuracy and computational efficiency. |
| format | Article |
| id | doaj-art-ba2516587dcf47c2b25bc08f7fe20cf9 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ba2516587dcf47c2b25bc08f7fe20cf92025-08-20T02:16:45ZengNature PortfolioScientific Reports2045-23222025-02-0115111410.1038/s41598-025-90016-0A deep learning based prediction model for effective elastic properties of porous materialsChang Liu0Ran Guo1Yangming Su2Faculty of Civil Engineering and Mechanics, Kunming University of Science and TechnologyFaculty of Civil Engineering and Mechanics, Kunming University of Science and TechnologyFaculty of Civil Engineering and Mechanics, Kunming University of Science and TechnologyAbstract With the development of the economy and society, porous materials have been widely used in various fields due to their unique structure and function. Therefore, it is of great significance to analyze the mechanical properties of porous materials. In traditional analysis methods, experimental and numerical simulation methods are mainly used. When conducting finite element numerical simulation analysis on porous materials, a large number of fine grids need to be divided, and the calculation process is time-consuming and laborious. This article randomly generates porous microstructure models through algorithms and uses efficient quadtree algorithms to calculate their mechanical properties, thereby obtaining a large amount of machine-learning sample data. Furthermore, a neural network-based machine learning algorithm is established to predict the mechanical properties of porous materials. By using microstructure images as the input layer of the model, the mechanical properties under corresponding conditions can be directly predicted. This study provides a new method for predicting mechanical properties based on microstructure images. It has been verified that the mechanical properties directly predicted by the network are similar to the actual ones, with high accuracy and computational efficiency.https://doi.org/10.1038/s41598-025-90016-0Deep learningPorous materialsQuadtree algorithmConvolutional neural network (CNN) |
| spellingShingle | Chang Liu Ran Guo Yangming Su A deep learning based prediction model for effective elastic properties of porous materials Scientific Reports Deep learning Porous materials Quadtree algorithm Convolutional neural network (CNN) |
| title | A deep learning based prediction model for effective elastic properties of porous materials |
| title_full | A deep learning based prediction model for effective elastic properties of porous materials |
| title_fullStr | A deep learning based prediction model for effective elastic properties of porous materials |
| title_full_unstemmed | A deep learning based prediction model for effective elastic properties of porous materials |
| title_short | A deep learning based prediction model for effective elastic properties of porous materials |
| title_sort | deep learning based prediction model for effective elastic properties of porous materials |
| topic | Deep learning Porous materials Quadtree algorithm Convolutional neural network (CNN) |
| url | https://doi.org/10.1038/s41598-025-90016-0 |
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