2020
DOI: 10.3390/polym12061371
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Characterization and Model Validation for Large Format Chopped Fiber, Foamed, Composite Structures Made from Recycled Olefin Based Polymers

Abstract: The purpose of this research is to predict the material performance of large format foamed core composite structures, such as crossties or structural timbers, using only constitutive properties. These structures are fabricated from recycled post-consumer/post-industrial waste composed of High-Density Polyethylene (HDPE) and Glass Filled Polypropylene (GFPP). A technical challenge in predicting the final part performance is the mathematical correlation between the microstructural variations and the macroscopic … Show more

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Cited by 3 publications
(7 citation statements)
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“…For the overall sample size of 7.38 × 5.38 inches, the image is approximately 3000 × 2300 pixels, and the size of each pixel is 63.5 µm. For the composite structure discussed, the radius of the fibers was measured to be 8.56 µm ± 0.57 µm [39] and thus the fibers are not visible within the image. Optical microscopy was performed on the surface of the cells, and the fibers did not protrude from the cell wall.…”
Section: Image Processing To Generate Cellsmentioning
confidence: 99%
See 4 more Smart Citations
“…For the overall sample size of 7.38 × 5.38 inches, the image is approximately 3000 × 2300 pixels, and the size of each pixel is 63.5 µm. For the composite structure discussed, the radius of the fibers was measured to be 8.56 µm ± 0.57 µm [39] and thus the fibers are not visible within the image. Optical microscopy was performed on the surface of the cells, and the fibers did not protrude from the cell wall.…”
Section: Image Processing To Generate Cellsmentioning
confidence: 99%
“…Based on the previous research [39], material properties were predicted using only constitutive properties. This was accomplished using the inputs of raw materials, including as elastic moduli and Poisson's ratios of HDPE and GFPP, glass fiber aspect ratio, and foamed core density.…”
Section: Materials Properties Predictionmentioning
confidence: 99%
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