A statistical homogenization approach for incorporating fiber aspect ratio distribution in large area polymer composite deposition additive manufacturing property predictions AITranslate
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Assessing the material stiffness of fiber reinforced polymer composites deposited in Large Area Additive Manufacturing (LAAM) is needed to define the process-structure-property mapping for the LAAM technology. While the screw-extrusion-based LAAM systems yield a distribution of fiber aspect ratio (i.e., length to diameter ratio for cylindrical inclusions) within deposited beads, most composite micromechanical models ignore the fiber geometry variation and instead assume a single value of fiber aspect ratio. This paper presents a statistics-based homogenization approach for including the fiber aspect ratio distribution in the prediction of the elastic properties of an extruded polymer composite bead. The fiber length distribution of a 13 wt% Carbon Fiber reinforced Acrylonitrile Butadiene Styrene (CF-ABS) processed through a LAAM deposition system is measured using high resolution optical microscopy. The Weibull probability distribution function is employed to statistically describe the measured values. The fitted probability density function is then incorporated into a fiber orientation homogenization approach to compute the variability of the elastic properties of the extruded composite. Elastic properties predicted by our proposed method are shown to differ from those presented in prior studies that ignored fiber length variability. The fiber aspect ratio reduction from fiber length attrition during the LAAM single screw-extrusion is shown to decrease the predicted flow-direction effective elastic modulus by 7%. Elastic moduli computed using our measured fiber aspect ratio distribution and proposed homogenization approach compare well to reported data from previously associated experimental studies.
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DOI:https://doi.org/10.1016/j.addma.2021.102006
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Assessing the material stiffness of fiber reinforced polymer composites deposited in Large Area Additive Manufacturing (LAAM) is needed to define the process-structure-property mapping for the LAAM technology. While the screw-extrusion-based LAAM systems yield a distribution of fiber aspect ratio (i.e., length to diameter ratio for cylindrical inclusions) within deposited beads, most composite micromechanical models ignore the fiber geometry variation and instead assume a single value of fiber aspect ratio. This paper presents a statistics-based homogenization approach for including the fiber aspect ratio distribution in the prediction of the elastic properties of an extruded polymer composite bead. The fiber length distribution of a 13 wt% Carbon Fiber reinforced Acrylonitrile Butadiene Styrene (CF-ABS) processed through a LAAM deposition system is measured using high resolution optical microscopy. The Weibull probability distribution function is employed to statistically describe the measured values. The fitted probability density function is then incorporated into a fiber orientation homogenization approach to compute the variability of the elastic properties of the extruded composite. Elastic properties predicted by our proposed method are shown to differ from those presented in prior studies that ignored fiber length variability. The fiber aspect ratio reduction from fiber length attrition during the LAAM single screw-extrusion is shown to decrease the predicted flow-direction effective elastic modulus by 7%. Elastic moduli computed using our measured fiber aspect ratio distribution and proposed homogenization approach compare well to reported data from previously associated experimental studies.
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| GB/T 7714-2015 | [1] Zhaogui Wang, Douglas E. Smith, David A. Jack. Additive Manufacturing, 2021(43). DOI:10.1016/j.addma.2021.102006. |
| MLA | [1] Zhaogui Wang, et al., Additive Manufacturing, no. 43, 2021, https://doi.org/10.1016/j.addma.2021.102006. |
| APA | [1] Zhaogui Wang, Douglas E. Smith, & David A. Jack. (2021). Additive Manufacturing(43). https://doi.org/10.1016/j.addma.2021.102006 |
| IEEE | [1] Zhaogui Wang, Douglas E. Smith, and David A. Jack, Additive Manufacturing, no. 43, 2021, doi: 10.1016/j.addma.2021.102006. |
