Computational and experimental investigation of reinforced polymers for material extrusion additive manufacturing

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Watanabe, Narumi
Rosen, David W.
Shofner, Meisha L.
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Among the most widely used additive manufacturing technologies is the material extrusion based process, in which a filament of thermoplastic material is liquefied and extruded through a nozzle to build a three-dimensional object in a layer-upon-layer fashion. One of the challenges of this technology is the limited availability of materials. In order to expand the portfolio of available materials, while reducing the cost of existing material productions, polypropylene-based polymers and composite materials for material extrusion additive manufacturing are investigated. However, since polypropylene is a semi-crystalline thermoplastic, a three-dimensional part fabricated with this material has a tendency to warp. In this thesis, material extrusion process simulation models are developed that are capable of predicting the temperature distributions, deposited filament shapes, residual stresses and warpages/deformations of fabricated parts. An alternative to reduce warpage of polypropylene parts is to create composite materials by combining with additives. Therefore, these process simulation models support the development of new materials by predicting part warpages quickly and cost effectively without the need of iterative experiments. These material extrusion process simulation models can be applied to both the quality and performance of fabricated parts, such as warpage and mechanical property anisotropy. The correlations between process variable settings on additive manufacturing machines and material properties of polypropylene-based composite materials on warpage characteristics are determined. In addition, the correlations between mechanical property anisotropy and the bonding quality of extruded filaments are examined experimentally by producing tensile property data of fabricated parts with different fill angles. The efficacy of the process simulation models are then evaluated by comparing the experimental and simulation model results.
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