Details
| Original language | English |
|---|---|
| Article number | 104726 |
| Journal | Mechanics of materials |
| Volume | 184 |
| Early online date | 30 Jun 2023 |
| Publication status | Published - Sept 2023 |
Abstract
Computational material modeling using advanced numerical techniques speeds up the design process and reduces the costs of developing new engineering products. In the field of multiscale modeling, huge computation efforts are expected for modeling heterogeneous materials while trying to reach high accuracy levels. In this work, a machine learning approach, namely the convolutional neural network (CNN), is developed as a solution providing a high level of accuracy while being computationally efficient. The input for the CNN model consists of two/three-dimensional images of artificial periodic and biphasic microstructures in the form of nonoverlapping and overlapping, mono- and polydisperse circular/spherical inclusion systems, which are generated by a random sequential inhibition process. These correspond to Statistical Volume Elements (SVE). Considering linear magnetostatics at the microscale, the output is the apparent permeability of the SVE. Training and testing data for the apparent properties is produced with finite element method-based two-scale asymptotic homogenization. The model efficiency is revealed by employing some representative examples in two and three-dimensional settings. In this regard, the performance of the CNN model is assessed with the applied computational homogenization method relating to the accuracy and computational efficiency. The results with the CNN model show high accuracy in predicting the homogenized permeability and a significant decrease in computation time.
Keywords
- Convolutional Neural Network (CNN), Homogenization, Magnetostatics
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Instrumentation
- Materials Science(all)
- General Materials Science
- Engineering(all)
- Mechanics of Materials
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In: Mechanics of materials, Vol. 184, 104726, 09.2023.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Machine learning aided multiscale magnetostatics
AU - Aldakheel, Fadi
AU - Soyarslan, Celal
AU - Palanisamy, Hari Subramani
AU - Elsayed, Elsayed Saber
N1 - Funding Information: Fadi Aldakheel (FA) gratefully acknowledges support for this research by the “German Research Foundation” (DFG) in the International Research Training Group (IRTG) 2657 program (Grant Reference Number 433082294 ).
PY - 2023/9
Y1 - 2023/9
N2 - Computational material modeling using advanced numerical techniques speeds up the design process and reduces the costs of developing new engineering products. In the field of multiscale modeling, huge computation efforts are expected for modeling heterogeneous materials while trying to reach high accuracy levels. In this work, a machine learning approach, namely the convolutional neural network (CNN), is developed as a solution providing a high level of accuracy while being computationally efficient. The input for the CNN model consists of two/three-dimensional images of artificial periodic and biphasic microstructures in the form of nonoverlapping and overlapping, mono- and polydisperse circular/spherical inclusion systems, which are generated by a random sequential inhibition process. These correspond to Statistical Volume Elements (SVE). Considering linear magnetostatics at the microscale, the output is the apparent permeability of the SVE. Training and testing data for the apparent properties is produced with finite element method-based two-scale asymptotic homogenization. The model efficiency is revealed by employing some representative examples in two and three-dimensional settings. In this regard, the performance of the CNN model is assessed with the applied computational homogenization method relating to the accuracy and computational efficiency. The results with the CNN model show high accuracy in predicting the homogenized permeability and a significant decrease in computation time.
AB - Computational material modeling using advanced numerical techniques speeds up the design process and reduces the costs of developing new engineering products. In the field of multiscale modeling, huge computation efforts are expected for modeling heterogeneous materials while trying to reach high accuracy levels. In this work, a machine learning approach, namely the convolutional neural network (CNN), is developed as a solution providing a high level of accuracy while being computationally efficient. The input for the CNN model consists of two/three-dimensional images of artificial periodic and biphasic microstructures in the form of nonoverlapping and overlapping, mono- and polydisperse circular/spherical inclusion systems, which are generated by a random sequential inhibition process. These correspond to Statistical Volume Elements (SVE). Considering linear magnetostatics at the microscale, the output is the apparent permeability of the SVE. Training and testing data for the apparent properties is produced with finite element method-based two-scale asymptotic homogenization. The model efficiency is revealed by employing some representative examples in two and three-dimensional settings. In this regard, the performance of the CNN model is assessed with the applied computational homogenization method relating to the accuracy and computational efficiency. The results with the CNN model show high accuracy in predicting the homogenized permeability and a significant decrease in computation time.
KW - Convolutional Neural Network (CNN)
KW - Homogenization
KW - Magnetostatics
UR - http://www.scopus.com/inward/record.url?scp=85166626338&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2301.12782
DO - 10.48550/arXiv.2301.12782
M3 - Article
AN - SCOPUS:85166626338
VL - 184
JO - Mechanics of materials
JF - Mechanics of materials
SN - 0167-6636
M1 - 104726
ER -