journal article·2018-10

Thermal and mechanical properties of 3D printed boron nitride – ABS composites

Tyler J. Quill, Matthew K. Smith, Tony Zhou, Mohamed Gamal Shafik Baioumy, Joao Paulo Berenguer, Baratunde A. Cola, Kyriaki Kalaitzidou, Thomas L. Bougher

Applied Composite Materials· Volume 25· Issue 5
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Abstract

The current work investigates the thermal conductivity and mechanical properties of Boron Nitride (BN)-Acrylonitrile Butadiene Styrene (ABS) composites prepared using both 3D printing and injection molding. The thermally conductive, yet electrically insulating composite material provides a unique combination of properties that make it desirable for heat dissipation and packaging applications in electronics. Materials were fabricated via melt mixing on a twin-screw compounder, then injection molded or extruded into filament for fused deposition modeling (FDM) 3D printing. Compositions of up to 35 wt.% BN in ABS were prepared, and the infill orientation of the 3D printed composites was varied to investigate the effect on properties. Injection molding produced a maximum in-plane conductivity of 1.45 W/m-K at 35 wt.% BN, whereas 3D printed samples of 35 wt.% BN showed a value of 0.93 W/m-K, over 5 times the conductivity of pure ABS. The resulting thermal conductivity is anisotropic; with the through-plane thermal conductivity lower by a factor of ~3 for injection molding and ~4 for 3D printing. Adding BN flakes caused a modest increase in the flexural modulus, but resulted in a large decrease in the flexural strength and impact toughness. It is shown that although injection molding produces parts with superior thermal and mechanical properties, BN shows much potential as a filler material for rapid prototyping of thermally conductive composites.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Additive Manufacturing and 3D Printing Technologies · Thermal properties of materials · Injection Molding Process and Properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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