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Fiber Orientation Predictions—A Review of Existing Models

Corporate Sector Research and Advance Engineering, Robert Bosch GmbH, 71272 Renningen, Germany
Polymer Engineering Center, University of Wisconsin-Madison, Madison, WI 53706, USA
Author to whom correspondence should be addressed.
J. Compos. Sci. 2020, 4(2), 69;
Received: 11 May 2020 / Revised: 29 May 2020 / Accepted: 2 June 2020 / Published: 8 June 2020
(This article belongs to the Special Issue Discontinuous Fiber Composites, Volume II)
Fiber reinforced polymers are key materials across different industries. The manufacturing processes of those materials have typically strong impact on their final microstructure, which at the same time controls the mechanical performance of the part. A reliable virtual engineering design of fiber-reinforced polymers requires therefore considering the simulation of the process-induced microstructure. One relevant microstructure descriptor in fiber-reinforced polymers is the fiber orientation. This work focuses on the modeling of the fiber orientation phenomenon and presents a historical review of the different modelling approaches. In this context, the article describes different macroscopic fiber orientation models such as the Folgar-Tucker, nematic, reduced strain closure (RSC), retarding principal rate (RPR), anisotropic rotary diffusion (ARD), principal anisotropic rotary diffusion (pARD), and Moldflow rotary diffusion (MRD) model. We discuss briefly about closure approximations, which are a common mathematical element of those macroscopic fiber orientation models. In the last section, we introduce some micro-scale numerical methods for simulating the fiber orientation phenomenon, such as the discrete element method (DEM), the smoothed particle hydrodynamics (SPH) method and the moving particle semi-implicit (MPS) method. View Full-Text
Keywords: fiber orientation; fiber reinforced thermoplastics; modeling fiber orientation; fiber reinforced thermoplastics; modeling
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Kugler, S.K.; Kech, A.; Cruz, C.; Osswald, T. Fiber Orientation Predictions—A Review of Existing Models. J. Compos. Sci. 2020, 4, 69.

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