An ANN-Based Prediction Method for Mechanical Properties of Hybrid Reinforced Composites 


Vol. 27,  No. 3, pp. 1495-1516, Mar.  2026
10.1007/s12221-025-01294-4


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  Abstract

The design of conventional three-dimensional (3D) orthogonal woven composites often relies on extensive theoretical computations, time-consuming simulations, or costly experimental testing. These methods involve high expenses and extended development cycles, which pose significant challenges to rapid design processes. This study develops a parametric multi-scale finite element (FE) model along with a corresponding artificial neural network (ANN) surrogate model for predicting the elastic properties of three-dimensional orthogonal woven composites. The FE model systematically investigates the influence of various parameters on elastic constants, demonstrating less than 4% deviation in both tensile and shear moduli compared to mechanical tests. An automated workflow bridging TexGen and Abaqus was employed to generate a dataset of 4655 samples, encompassing variations in microstructural composition, yarn fabrication, preform weaving, and curing conditions. Based on this dataset, an artificial neural network-based surrogate model was trained, achieving a mean absolute percentage error of only 2.34% relative to the full FE simulations, while reducing computational time by a factor of 58,000. This integrated framework provides a robust foundation for the rapid design and optimization of 3D orthogonal woven composites, establishing an efficient pathway for the development of advanced fiber-reinforced materials.

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  Cite this article

[IEEE Style]

J. Ke, R. Li, J. Zhang, Z. Wu, Z. Le, W. Lu, "An ANN-Based Prediction Method for Mechanical Properties of Hybrid Reinforced Composites," Fibers and Polymers, vol. 27, no. 3, pp. 1495-1516, 2026. DOI: 10.1007/s12221-025-01294-4.

[ACM Style]

Jun Ke, Rongkun Li, Jiaqiang Zhang, Zhenyu Wu, Zhongping Le, and Wenqi Lu. 2026. An ANN-Based Prediction Method for Mechanical Properties of Hybrid Reinforced Composites. Fibers and Polymers, 27, 3, (2026), 1495-1516. DOI: 10.1007/s12221-025-01294-4.

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