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Numerical Characterization of Permeability Tensor of Fiber Preforms for Liquid Composite Molding Applications

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Composite materials are of paramount importance in aeronautics, aerospace and automotive applications. Consisting of fibers and matrix keeping the fibers together, composite materials offer high strength and low weight. In one of the most common composite manufacturing method, Liquid Composite Molding (LCM) process, reinforcing glass, carbon or Kevlar fiber preforms are placed in a mold cavity and a liquid resin is introduced to cover the remaining empty space to form a composite by curing the resin. The primary objective in LCM processes is the successful filling of the preform with resin system. In order to obtain a desired composite product, the process needs to be modeled to have an insight about the filling and curing stages of the LCM process. The fiber preform permeability plays a key role in the filling pattern of the mold, which dictates if there will be any voids in the composites. Thus, to achieve a successful model of the LCM process, one needs to characterize the permeability tensor to quantify the resistance to resin flow through fibers. Besides various experimental and numerical techniques to characterize the permeability, this study offers a new numerical methodology to characterize the permeability of a unit cell of a fiber system. The study involves the characterization of the permeability of a unit cell under various shear strains and project a permeability map from the unit cell characterizations on the macro scale including the drape effect.
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Keywords: LIQUID COMPOSITE MOLDING; PERMEABILITY MAP; PROCESS DESIGN

Document Type: Research Article

Publication date: July 1, 2019

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  • Advanced Science, Engineering and Medicine (ASEM) is a science, engineering, technical and medical journal focused on the publishing of peer-reviewed multi-disciplinary research articles dealing with all fundamental and applied research aspects in the areas of (1) Physical Sciences, (2) Engineering, (3) Biological Sciences/Health Sciences, (4) Medicine, (5) Computer and Information Sciences, (6) Mathematical Sciences, (7) Agriculture Science and Engineering, (8) Geosciences, and (9) Energy/Fuels/Environmental/Green Science and Engineering.
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