• Composites Research
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• v.36 no.5
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• pp.329-334
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• 2023

The classical multiscale finite element (FE² ) method involves iterative calculations of micro-boundary value problems for representative volume elements at every integration point in macro scale, making it a computationally time and data storage space. To overcome this, we developed the data-driven multiscale analysis method based on the mean-field homogenization (MFH). Data-driven computational mechanics (DDCM) analysis is a model-free approach that directly utilizes strain-stress datasets. For performing multiscale analysis, we efficiently construct a strain-stress database for the microstructure of composite materials using mean-field homogenization and conduct data-driven computational mechanics simulations based on this database. In this paper, we apply the developed multiscale analysis framework to an example, confirming the results of data-driven computational mechanics simulations considering the microstructure of a hyperelastic composite material. Therefore, the application of data-driven computational mechanics approach in multiscale analysis can be applied to various materials and structures, opening up new possibilities for multiscale analysis research and applications.

• Composites Research
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• v.36 no.3
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• pp.205-210
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• 2023

In this study, we developed a transfer learning framework based on homogenization data for efficient prediction of the effective mechanical properties and thermal conductivity of cellular foam structures. Mean-field homogenization (MFH) based on the Eshelby's tensor allows for efficient prediction of properties in porous structures including ellipsoidal inclusions, but accurately predicting the properties of cellular foam structures is challenging. On the other hand, finite element homogenization (FEH) is more accurate but comes with relatively high computational cost. In this paper, we propose a data-driven transfer learning framework that combines the advantages of mean-field homogenization and finite element homogenization. Specifically, we generate a large amount of mean-field homogenization data to build a pre-trained model, and then fine-tune it using a relatively small amount of finite element homogenization data. Numerical examples were conducted to validate the proposed framework and verify the accuracy of the analysis. The results of this study are expected to be applicable to the analysis of materials with various foam structures.

• Composites Research
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• v.33 no.2
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• pp.81-89
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• 2020

In this review paper, we introduce recent research studied effective physical properties of the reinforced composite using mean-field homogenization. We address homogenization for effective stiffness and expand it to effective thermal/electrical conductivity and dielectric constant. Multiphysics problems like piezoelectricity and thermoelectricity are considered by simplifying the constitutive equation into the linear equations like Hooke's law. We present a generalized theoretical formula for predicting effective physical properties of composite and validation by against finite element analysis.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.2
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• pp.81-93
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• 2020

The fatigue characteristics of glass fiber reinforced plastic (GFRP) composites were studied under repeated loads using the finite element method (FEM). To realize the material characteristics of GFRP composites, Digimat, a mean-field homogenization tool, was employed. Additionally, the micro-structures and material models of GFRP composites were defined with it to predict the fatigue behavior of composites more realistically. Specifically, the fatigue characteristics of polybutylene terephthalate with short fiber fractions of 30wt% were investigated with respect to fiber orientation, stress ratio, and thickness. The injection analysis was conducted using Moldflow software to obtain the information on fiber orientations. It was mapped over FEM concerned with fatigue specimens. LS-DYNA, a typical finite element commercial software, was used in the coupled analysis of Digimat to calculate the stress amplitude of composites. FEMFAT software consisting of various numerical material models was used to predict the fatigue life. The results of coupled analysis of linear and nonlinear material models of Digimat were analyzed to identify the fatigue characteristics of GFRP composites using FEMFAT. Neuber's rule was applied to the linear material model to analyze the fatigue behavior in LCF regimen. Additionally, to evaluate the morphological and mechanical structure of GFRP composites, the coupled and fatigue analysis were conducted in terms of thickness.

• Steel and Composite Structures
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• v.39 no.2
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• pp.215-228
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• 2021

This paper investigates the effect of micromechanical models on the bending behavior of bidirectional functionally graded (BDFG) beams subjected to different mechanical loading. The material properties of the beam are considered to be graded in both axial and thickness directions according to a power law. The beam's behavior is modeled by the mean of quasi 3D displacement field that contain undetermined integral terms and involves a reduced unknown functions. Navier's method is employed to determine and compute the displacements and stress for a simply supported beam. Different homogenization schemes such as Voigt, Reus, and Mori-Tanaka are employed to analyze the response of the BDFG beam subjected to linear, uniform, exponential and sinusoidal distributed loading. The results obtained by the present method are compared with available results in the literature and a good agreement was found. Several numerical results are presented in tabular form and in figures to examine the effects of the material gradation, micromechanical models and types of loading on the bending response of BDFG beams. It can be concluded that the present theory is not only accurate but also simple in predicting the bending response of BDFG beam subjected to different static loads.