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Prediction of Young’s Modulus for Injection Molded Long Fiber Reinforced Thermoplastics

Department of Chemical Engineering, Virginia Tech, VA 24061, USA
Macromolecules Innovation Institute, Virginia Tech, VA 24061, USA
Author to whom correspondence should be addressed.
J. Compos. Sci. 2018, 2(3), 47;
Received: 26 June 2018 / Revised: 17 July 2018 / Accepted: 2 August 2018 / Published: 6 August 2018
(This article belongs to the Special Issue Discontinuous Fiber Composites)
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In this article, the elastic properties of long-fiber injection-molded thermoplastics (LFTs) are investigated by micro-mechanical approaches including the Halpin-Tsai (HT) model and the Mori-Tanaka model based on Eshelby’s equivalent inclusion (EMT). In the modeling, the elastic properties are calculated by the fiber content, fiber length, and fiber orientation. Several closure approximations for the fourth-order fiber orientation tensor are evaluated by comparing the as-calculated elastic stiffness with that from the original experimental fourth-order tensor. An empirical model was developed to correct the fibers’ aspect ratio in the computation for the actual as-formed LFTs with fiber bundles under high fiber content. After the correction, the analytical predictions had good agreement with the experimental stiffness values from tensile tests on the LFTs. Our analysis shows that it is essential to incorporate the effect of the presence of fiber bundles to accurately predict the composite properties. This work involved the use of experimental values of fiber orientation and serves as the basis for computing part stiffness as a function of mold filling conditions. The work also explains why the modulus tends to level off with fiber concentration. View Full-Text
Keywords: long glass fibers; elastic properties; fiber orientation; fiber length; fiber bundles long glass fibers; elastic properties; fiber orientation; fiber length; fiber bundles

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Chen, H.; Baird, D.G. Prediction of Young’s Modulus for Injection Molded Long Fiber Reinforced Thermoplastics. J. Compos. Sci. 2018, 2, 47.

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