• Composites Research
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• v.12 no.5
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• pp.31-39
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• 1999

Glass-fiber reinforced polymeric composites provide the desitable properties of high stiffness and strength as well as specific weight. Hence, they have become some of the most important materials in several industries. These composites can be grouped into thermoplastic and thermoset composites, with thermoplastic composites having several advantages over thermoset composites in mechanical properties and processing. As a result, the study of the material behavior and forming techniques of such composites has attracted considerable attention in recent years. When the continuous fiber-reinforced polymeric composites are molded by flow molding, the molded parts leads to be nonhomogeneity and anisotropic because of the separation and orientation of fibers. As the characteristics of the products are greatly dependent on the separation, it is very important to clarify the separation in relarion to molding conditions, fiber mat structures and mold geometry. In this study, the effects of the mold geometry and the fiber mat structure on the compression moldability are studied using the cup-type molding.

• Composites Research
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• v.36 no.1
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• pp.48-52
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• 2023

Fiber-reinforced composites have anisotropic material properties, so the mechanical properties of composite structures can vary depending on the stacking sequence. Therefore, it is essential to design the proper stacking sequence of composite structures according to the functional requirements. However, depending on the manufacturing condition or the shape of the structure, there are many cases where the designed stacking angle is out of range, which can affect structural performance. Accordingly, it is important to analyze the stacking angle in order to confirm that the composite structure is correctly fabricated as designed. In this study, the stacking angle was predicted from real cross-sectional images of fiber-reinforced composites using convolutional neural network (CNN)-based deep learning. Carbon fiber-reinforced composite specimens with several stacking angles were fabricated and their cross-sections were photographed on a micro-scale using an optical microscope. The training was performed for a CNN-based deep learning model using the cross-sectional image data of the composite specimens. As a result, the stacking angle can be predicted from the actual cross-sectional image of the fiber-reinforced composite with high accuracy.

• Journal of the Korean Geotechnical Society
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• v.24 no.1
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• pp.81-89
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• 2008

The purpose of this study is to develop a new technique to quickly assess the quantitative stability of a tunnel by using measured displacement at the tunnel construction site. To achieve this purpose, in this study, a critical strain concept was introduced for the first time and applied to an assessment of a tunnel under construction. The new technique calculates numerically the strains of the surrounding ground by using displacements measured during tunnel excavation. The techniques considering the relative displacement, shotcrete, and anisotropic characteristics of ground were newly introduced after reinvestigating the existing analysis technique. In addition, an analysis module was developed based on the proposed analysis technique in this study, and the applicability of the developed module was verified. To verify the module, first of all, the calculated excavation displacements of a cylindrical tunnel by analytic method and commercial programs (Pentagon-3D, Flac-2D) were compared for the confirmation of applicability of commercial programs. Then, the calculated excavation displacements under the same initial condition, both with and without a shotcrete lining, by two commercial programs were compared. finally, we assess the load condition and material properties of in-situ ground by inputting tunnel excavation displacement, which was calculated by a commercial program, into the developed analysis module (FAST-Ver. 1.2, feedback Analysis System for Tunneling), and checked whether the assessed results conform to the originally assumed values.