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Technical Paper

Effect of Fiber Orientation on the Mechanical Properties of Long Glass Fiber Reinforced (LGFR) Composites

2014-04-01
2014-01-1049
Long glass fiber reinforced (LGFR) composites have been widely used in automotive industry to reduce vehicle weight and maintain relatively high mechanical performances. Due to the injection molding process, the distribution of fiber orientations varies at different locations and through the panel thickness, resulting in anisotropic and non-uniform mechanical properties. The current practice of computer modeling of these materials is generally using isotropic properties adjusted by a certain scale factor. The effect of fiber orientation is not carefully considered due to the complexity of fiber orientation distribution in the LGFR parts. The purpose of this paper is to identify key factors affecting vehicle attribute performances where LGFR composites are used; and provide an efficient way for accurate CAE modeling of LGFR composites. In this study, tensile coupons cut from a simple geometric injection molded plaque are tested.
Technical Paper

Fatigue Life Prediction of Injection Molding Tool

2017-03-28
2017-01-0340
Injection molding tools are expensive and the fatigue failure during production would result in very costly rework on the tool and downtime. Currently, mold designs are mostly based on expert experience without a careful stress analysis and the mold tool life cycle relies largely on rough estimates. The industry state of the art applies averaged temperature change and peak pressure load on the mold tool. The static analysis is then performed. Mold temperature history and thermal shock are not considered in the durability analysis. In this paper, a transient thermal analysis of the tool is performed in conjunction with the injection molding process simulation. The spatial and temporal variation of temperature, pressure and clamping forces are exported from Moldflow simulation. These histories of temperature and pressure are converted to appropriate loading curves and mapped into Abaqus FEA model.
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