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  5. Correlating Large-Format Additive Manufacturing Processing Parameters to Fiber Length and the Mechanical Performance of Reinforced Polymer Composites
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Correlating Large-Format Additive Manufacturing Processing Parameters to Fiber Length and the Mechanical Performance of Reinforced Polymer Composites

Date Issued
May 1, 2023
Author(s)
Rhodes, Andrew Phillip
Advisor(s)
Chad E. Duty
Additional Advisor(s)
Brett G. Compton
Uday K. Vaidya
Chad E. Duty
Permanent URI
https://trace.tennessee.edu/handle/20.500.14382/45867
Abstract

The Big Area Additive Manufacturing (BAAM) system at Oak Ridge National Laboratory has been used to produce carbon fiber reinforced structures for several years, including vehicles, building constituents, composite tooling, etc. While the development of a large-format polymer additive manufacturing (AM) system has moved quickly, the impact of the BAAM’s extruder on the length of carbon fiber feedstock has not been systematically studied. Numerous studies in processing fiber reinforced thermoplastics in plasticizing and injection molding systems have shown that fibers are subjected to significant shear as they are processed, which can cause a drastic reduction in fiber length which negatively affects tensile strength. Recent efforts to improve mechanical properties have focused on interfacial properties and addressing meso-structural defects, so this leaves significant opportunities to improve mechanical properties of printed structures by understanding the degree of fiber length degradation in processing. Understanding the relationship between fiber degradation, processing speed, and tensile properties, will give users of any large-scale, screw-extrusion AM system a powerful tool to better tailor the mechanical properties of their parts to the application.

Subjects

Additive manufacturin...

processing

fiber length

measurement

Disciplines
Manufacturing
Mechanical Engineering
Other Materials Science and Engineering
Degree
Master of Science
Major
Mechanical Engineering
File(s)
Thumbnail Image
Name

Andrew_Rhodes_Master_s_Thesis_Rev_3.docx

Size

27.4 MB

Format

Microsoft Word XML

Checksum (MD5)

81507f539ab5bf372c33dc27061491fe

Thumbnail Image
Name

auto_convert.pdf

Size

2.42 MB

Format

Adobe PDF

Checksum (MD5)

13f35a217bac700e8a54131b42918d0d

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