• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.5
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• pp.331-338
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• 2020

In this study, the characteristic of a 3-D micro-woven material, which is one of the newly developed periodic open-cell structure, is analyzed through various computational simulations. To increase the accuracy of the numerical simulations, the distance between each directional wire is parameterized using six design variables, and its model geometry is precisely discretized using tetrahedron elements. Using the improved computational model, the material properties of the mechanical, thermal, and fluidic behavior are investigated using commercial software and compared with the previous experimental results. By changing the space between the x- and y-directional wires, a parametric test is performed to determine the tendency of the change in the material properties. In addition, the correlation between two different material properties is investigated using the Ashby chart. The result can further be used in determining the optimal pattern and wire spacing in 3-D micro-woven materials.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.259-264
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• 2023

In this study, we devised a parametric analysis workflow for efficiently analyzing the material properties of 3D woven materials. The parametric model uses wire spacing in the woven materials as a design parameter; we generated 2,500 numerical models with various combinations of these design parameters. Using MATLAB and ANSYS software, we obtained various material properties, such as bulk modulus, thermal conductivity, and fluid permeability of the woven materials, through a parametric batch analysis. We then used this large dataset of material properties to perform a regression analysis to validate the relationship between design variables and material properties, as well as the accuracy of numerical analysis. Furthermore, we constructed an artificial neural network capable of predicting the material properties of 3D woven materials on the basis of the obtained material database. The trained network can accurately estimate the material properties of the woven materials with arbitrary design parameters, without the need for numerical analyses.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.6
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• pp.351-356
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• 2022

In this paper, we present the regression analysis and design optimization for improving the permeability of 3D woven materials based on numerical analysis data. First, the parametric analysis model is generated with variables that define the gap sizes between each directional wire of the woven material. Then, material properties such as bulk modulus, thermal conductivity coefficient, and permeability are calculated using numerical analysis, and these material data are used in the polynomial-based regression analysis. The Pareto optimal solution is obtained between bulk modulus and permeability by using multi-objective optimization and shows their trade-off relation. In addition, gradient-based design optimization is applied to maximize the fluid permeability for 3D woven materials, and the optimal designs are obtained according to the various minimum bulk modulus constraints. Finally, the optimal solutions from regression equations are verified to demonstrate the accuracy of the proposed method.