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Stiffness Prediction and Validation of Large Volume 3D Printed, Short-Fiber-Filled Polymer Composites


Title: Stiffness Prediction and Validation of Large Volume 3D Printed, Short-Fiber-Filled Polymer Composites

Authors: Timothy Russell and David A. Jack

DOI: 10.33599/nasampe/s.19.1558

Abstract: Large-volume 3D deposition of short carbon fiber filled (CFF) thermoplastic composites greatly enhances the value and potential of additive manufacturing by offering fast production of large-scale tooling and even large-scale, end-use parts. Validated models to predict the material properties as a function of processing parameters of such 3D printed composites aid in driving down the design cost of a production part by eliminating the need to process large volumes of material in trial prints and the subsequent final product characterization. In this study, a method of predicting the effective elastic modulus from the flow simulation to the final deposition and cooling of a short fiber filled deposited structure is presented. Specifically, a 13% CFF acrylonitrile butadiene styrene (ABS) is considered. An in-house, large volume 3D printer was built and used to print tensile bars that were tested based off ASTM-D3039. Modeling was carried out using a custom MATLAB code to model the fiber orientation state along the velocity field streamlines within the nozzle, the die-swell of the extrudate, and the subsequent deposition onto the moving platen. The resulting predicted fiber orientation state is then coupled with micromechanical modeling to obtain a spatially varying anisotropic stiffness tensor. This result is then used within a finite element model with spatial varying stiffness to mimic the effective stiffness of the processed composite. Modeling results indicate little difference between a fully filled deposition (i.e., no interlaminar voids between deposition beads) and the actual cross-sectioned geometry. The results obtained from the RSC fiber interaction model for a value of κ=1/30 and C_I=0.03 were in the best agreement with the experimental testing with a differential of less than 20% between experiment and modeling, and future work will be required to better characterize the flow parameters before the modeling efforts can be considered fully validated.

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Conference: SAMPE 2019 - Charlotte, NC

Publication Date: 2019/05/20

SKU: TP19--1558

Pages: 14

Price: FREE

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