This article develops a methodology to predict the elastic properties of long-fiber injection-molded thermoplastics (LFTs). The corrected experimental fiber length distribution and the predicted and experimental orientation distributions were used in modeling to compute the elastic properties of the composite. First, from the fiber length distribution (FLD) data in terms of number of fibers versus fiber length, the probability density functions were built and used in the computation. The two-parameter Weibull's distribution was also used to represent the actual FLD. Next, the Mori-Tanaka model that employs the Eshelby's equivalent inclusion method was applied to calculate the stiffness matrix of the aligned fiber composite containing the established FLD. The stiffness of the actual as-formed composite was then determined from the stiffness of the computed aligned fiber composite that was averaged over all possible orientations using the orientation averaging method. The methodology to predict the elastic properties of LFTs was validated via experimental verification of the longitudinal and transverse moduli determined for long glass fiber injection-molded polypropylene specimens. Finally, a sensitivity analysis was conducted to determine the effect of a variation of FLD on the composite elastic properties. Our analysis shows that it is essential to obtain an accurate fiber
Recently, injection molded long-fiber thermoplastics (LFTs) have generated great interest within the automotive industry as these materials can potentially be used for structural applications to reduce vehicle weight. However, before our work on LFTs, injection-molding of these materials posed a great challenge because of two main reasons: (i) no process models for LFTs had been developed that could predict the injection-molding of an LFT part, and (ii) no experimental characterization methods had been developed to enable the characterization of the as-formed LFT microstructure to determine the fiber orientation and length distributions that are critical to any process model development. The objective of this project is twofold. First, the advanced process models for LFTs are developed that can accurately predict the composite microstructure governed by the flow-induced fiber orientation, fiber length distribution, and other features resulting from processing. Second, the models are implemented in the Autodesk Moldflow Insight software to enable the injection molding simulations of LFT structures. This report describes the work conducted under the Cooperative Research and Development Agreement (CRADA) (Nr. 260) between Battelle as Operator of the Pacific Northwest National Laboratory (PNNL) and Autodesk, Inc. PNNL has worked with the University of Illinois at Urbana-Champaign (UIUC) through subcontracts to develop process models for fiber orientation and length distributions in injection-molded LFTs. The UIUC has then developed and implemented the new process models for LFTs in an in-house code named ORIENT to enable first validations and applications of these models. Next, Autodesk, Inc. has implemented these new process models in Autodesk Moldflow Insight for LFT injection molding simulations. In addition, Autodesk, Inc. has performed rheological and mechanical tests to identify the rheological and physical properties for the pellet materials used in this CRADA. These properties are needed for injection-molding simulations using Autodesk Moldflow Insight. Autodesk, Inc. has delivered to PNNL the Autodesk Moldflow Insight research versions containing the newly implemented models for process simulations and also has provided PNNL with the technical support for the model validations. UIUC has assisted Autodesk, Inc. in the model implementations in Autodesk Moldflow Insight and also has assisted PNNL in the model validation using Autodesk Moldflow Insight by providing consultancy and reviews. Oak Ridge National Laboratory (ORNL) has performed microstructural characterizations for fiber length and orientation allowing the validations of the developed models.
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