Graph neural networks for mechanical property prediction of 2D fiber composites
This work investigates the ability of graph neural networks (GNNs) to homogenize 2D fiber composite microstructures. We use different inhomogeneity and anisotropy indices to motivate and show that the Volume Elements (VEs) used in ML methods should ideally be far from their Representative Volume Ele...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127525009207 |
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| author | Erdem Caliskan Reza Abedi Massimiliano Lupo Pasini |
| author_facet | Erdem Caliskan Reza Abedi Massimiliano Lupo Pasini |
| author_sort | Erdem Caliskan |
| collection | DOAJ |
| description | This work investigates the ability of graph neural networks (GNNs) to homogenize 2D fiber composite microstructures. We use different inhomogeneity and anisotropy indices to motivate and show that the Volume Elements (VEs) used in ML methods should ideally be far from their Representative Volume Element (RVE) size limit and, consequently, are notably anisotropic. Hence, training only the isotropic limit properties may not be acceptable. Another aspect is the need to normalize elastic stiffness values for ML, especially when high elastic contrast ratios are encountered between composite phases or in the material set. We introduce a normalization technique based on the mean-field method (MFM) to handle such high contrast ratios and train for the entire stiffness tensor. We show that the proposed GNN approaches exhibit high accuracy and efficiency compared to traditional methods and convolutional neural networks, utilizing unstructured graphs constructed from microstructure topology. Our model successfully predicts the stiffness tensor, peak strength under bulk damage, and brittle fracture initiation strength across diverse microstructure configurations while maintaining high accuracy even for extreme material contrasts and volume fractions. We also present a method to improve prediction accuracy for small dataset sizes using Voronoi partitioning. |
| format | Article |
| id | doaj-art-baa0e2ae37be45a8a95f58c245e173ac |
| institution | Kabale University |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-baa0e2ae37be45a8a95f58c245e173ac2025-08-20T03:41:31ZengElsevierMaterials & Design0264-12752025-09-0125711450010.1016/j.matdes.2025.114500Graph neural networks for mechanical property prediction of 2D fiber compositesErdem Caliskan0Reza Abedi1Massimiliano Lupo Pasini2Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee Knoxville, 1506 Middle Drive, Knoxville, 37916, TN, USADepartment of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee Knoxville, 1506 Middle Drive, Knoxville, 37916, TN, USAOak Ridge National Laboratory, Computational Sciences and Engineering Division, 1 Bethel Valley Rd, Oak Ridge, 37831, TN, USA; Corresponding author.This work investigates the ability of graph neural networks (GNNs) to homogenize 2D fiber composite microstructures. We use different inhomogeneity and anisotropy indices to motivate and show that the Volume Elements (VEs) used in ML methods should ideally be far from their Representative Volume Element (RVE) size limit and, consequently, are notably anisotropic. Hence, training only the isotropic limit properties may not be acceptable. Another aspect is the need to normalize elastic stiffness values for ML, especially when high elastic contrast ratios are encountered between composite phases or in the material set. We introduce a normalization technique based on the mean-field method (MFM) to handle such high contrast ratios and train for the entire stiffness tensor. We show that the proposed GNN approaches exhibit high accuracy and efficiency compared to traditional methods and convolutional neural networks, utilizing unstructured graphs constructed from microstructure topology. Our model successfully predicts the stiffness tensor, peak strength under bulk damage, and brittle fracture initiation strength across diverse microstructure configurations while maintaining high accuracy even for extreme material contrasts and volume fractions. We also present a method to improve prediction accuracy for small dataset sizes using Voronoi partitioning.http://www.sciencedirect.com/science/article/pii/S0264127525009207Machine learningFiber compositesBrittle strengthHomogenizationGraph neural network |
| spellingShingle | Erdem Caliskan Reza Abedi Massimiliano Lupo Pasini Graph neural networks for mechanical property prediction of 2D fiber composites Materials & Design Machine learning Fiber composites Brittle strength Homogenization Graph neural network |
| title | Graph neural networks for mechanical property prediction of 2D fiber composites |
| title_full | Graph neural networks for mechanical property prediction of 2D fiber composites |
| title_fullStr | Graph neural networks for mechanical property prediction of 2D fiber composites |
| title_full_unstemmed | Graph neural networks for mechanical property prediction of 2D fiber composites |
| title_short | Graph neural networks for mechanical property prediction of 2D fiber composites |
| title_sort | graph neural networks for mechanical property prediction of 2d fiber composites |
| topic | Machine learning Fiber composites Brittle strength Homogenization Graph neural network |
| url | http://www.sciencedirect.com/science/article/pii/S0264127525009207 |
| work_keys_str_mv | AT erdemcaliskan graphneuralnetworksformechanicalpropertypredictionof2dfibercomposites AT rezaabedi graphneuralnetworksformechanicalpropertypredictionof2dfibercomposites AT massimilianolupopasini graphneuralnetworksformechanicalpropertypredictionof2dfibercomposites |