Peridynamics-fueled convolutional neural network for predicting mechanical constitutive behaviors of fiber reinforced composites
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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Detail(s)
Original language | English |
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Article number | 117309 |
Journal / Publication | Computer Methods in Applied Mechanics and Engineering |
Volume | 431 |
Online published | 29 Aug 2024 |
Publication status | Published - 1 Nov 2024 |
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Abstract
Despite advancements in predicting the constitutive relationships of composite materials, characterizing the effects of microstructural randomness on their mechanical behaviors remains challenging. In this study, we propose a data-driven convolutional neural network (CNN) to efficiently predict the stress-strain curves containing three key material features (Tensile strength, modulus, and toughness) of fiber reinforced composites. Firstly, stress-strain curves for composites with arbitrary fiber distributions were generated using experimentally validated peridynamics (PD) model. Principal component analysis (PCA) was then employed to learn these curves in a lower-dimensional space, reducing computational costs. Subsequently, these reduced data, along with randomly distributed microstructural features, were used to train, validate, and evaluate the CNN models. The combined CNN and PCA model accurately predicted stress-strain curves with maximum errors of 2.5 % for tensile strength, 10% for modulus, and 20 % for toughness. Furthermore, data augmentation and Mean Squared Error (MSE) as a loss function significantly enhanced the model's prediction accuracy. Our findings indicated that DenseNet121 outperformed other CNN models in predicting the properties of fiber-reinforced materials, further demonstrating the effectiveness of the proposed model. This work successfully demonstrates the applicability of a data-driven CNN approach to predict stress-strain relations for engineering materials with intricate heterogeneous microstructures, paving the way for data-driven computational mechanics applied in composites. © 2024 Elsevier B.V.
Research Area(s)
- Convolutional neural networks, Fiber reinforced composites, Machine learning, Mechanical constitutive behaviors, Peridynamic modeling
Citation Format(s)
Peridynamics-fueled convolutional neural network for predicting mechanical constitutive behaviors of fiber reinforced composites. / Yin, Binbin; Huang, Jiasheng; Sun, Weikang.
In: Computer Methods in Applied Mechanics and Engineering, Vol. 431, 117309, 01.11.2024.
In: Computer Methods in Applied Mechanics and Engineering, Vol. 431, 117309, 01.11.2024.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review