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Nano-CT characterization of structural voids and air bubbles in fused deposition modeling for additive manufacturing
Conference paper

Nano-CT characterization of structural voids and air bubbles in fused deposition modeling for additive manufacturing

Roland K. Chen, Terris T. Lo, Lei Chen and Albert J. Shih
ASME 2015 International Manufacturing Science and Engineering Conference, MSEC 2015, Vol.1
2015

Abstract

Additive manufacturing Computed Tomography Fused deposition modeling Meso-structure Industrial and Manufacturing Engineering
The build quality of fused deposition modeling (FDM) parts depends on many build parameters, such as toolpath and temperature. Destructive material testing methods are widely used to examine FDM parts with different build parameters. The optimization of build parameters relies on methods of experimental design and extensive material testing. However, this approach mainly considers the bulk properties of the FDM part, without fully understanding the effect of each parameter on the build quality. This study presents a method to investigate the integrity of FDM parts using nano-focus computed tomography (NanoCT). A solid filled ULTEM sample was built and underwent NanoCT scan. The three dimensional geometry of this sample was reconstructed. Structural voids and bubbles inside the sample were also identified and quantified. The volume of this solid filled sample consists of 11.9% structural voids and bubbles. Air bubbles are further categorized into internal bubbles (bubbles inside the deposited fibers) and necking bubbles (bubbles at the bonding region of two adjacent fibers). While structural voids can be predicted according to toolpath, layer thickness, and extruder diameter, the occurrence of air bubbles are unexpected and can compromise the integrity of the built parts. NanoCT offers a non-destructive way to inspect the integrity of FDM parts. NanoCT can also be used to study three dimensional meso-structure and correlate that with build parameters. This will provide insightful information for further studying the FDM process and help to predict material strengths and to improve the part quality.

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