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Tensile Property Prediction of Long Fiber Thermoplastic Composites Manufactured by LFT-D-IM Process


Title: Tensile Property Prediction of Long Fiber Thermoplastic Composites Manufactured by LFT-D-IM Process

Authors: Chanwoo Joung, Yoon-Bo Shim, Dahun Lee, Bjoern Beck, Frank Henning, Young-Bin Park

DOI: 10.33599/nasampe/c.23.0215

Abstract: Direct compounding of long fiber thermoplastic followed by injection molding (LFT-D-IM) is a highly efficient, in-line thermoplastic composite manufacturing process that combines direct fiber compounding and injection molding. The process is cost-effective as it doesn’t require intermediate or semi-finished products. Continuous fiber rovings are cut and directly compounded with the polymer melt using a screw. As the cycle time can be reduced by process optimization and automation, the process has gained popularity as an alternative for mass production of large, geometrically complex parts. The main objective of the study is to develop a modeling-and-simulation-based method to predict the tensile properties of long glass fiber/polyamide 66 (LGF/PA66) composites manufactured by LFT-D-IM. The strategy combines: (1) prediction of polymer-melt-flow-induced fiber orientation by thermo-viscoelastic flow analysis; (2) flow analysis – finite element analysis mapping; and (3) micromechanics-based tensile property prediction. Flow analysis for injection molding was performed to calculate fiber orientation distribution. Based on the orientation distribution and micromechanics, the tensile properties of the composites were modeled, calculated, and applied on finite elements for virtual dog-bone specimens. Structural analysis of virtual tensile testing was conducted to calculate the tensile stress-strain curves and the tensile properties of the specimens. To validate the method developed, LGF/PA66 composite plates were manufactured by LFT-D-IM from which dog-bone specimens were prepared, and the tensile properties were characterized. The measured and predicted tensile properties of the specimens with different fiber lengths, contents, and orientation showed good agreement. The prediction method can be expanded to other LFT composites with varying fiber and matrix types.

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Conference: CAMX 2023

Publication Date: 2023/10/30

SKU: TP23-0000000215

Pages: 7

Price: $14.00

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