Torsion analysis of the anisotropic behavior of FDM technology View Full Text


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Article Info

DATE

2018-04

AUTHORS

Cesar Omar Balderrama-Armendariz, Eric MacDonald, David Espalin, David Cortes-Saenz, Ryan Wicker, Aide Maldonado-Macias

ABSTRACT

Several reports have studied the mechanical properties of the material extrusion additive manufacturing process, specifically referred to as fusion deposition modeling (FDM) developed by Stratasys. As the applications for 3D printed parts continue to grow in diversity (e.g., gears, propellers, and bearings), the loading conditions applied to printed parts have become more complex, and the need for thorough characterization is now paramount for increased adoption of 3D printing. To broaden the understanding of torsional properties, this study focused on the shear strength of specimens to observe the impact from additive manufacturing. A full factorial (42) design of experiments was used, considering the orientation and the raster angle as factors. XYZ, YXZ, ZXY, and XZY levels were considered for the orientation parameter, as well as 0°, 45°, 90°, and 45°/45° for the raster angle parameter. Ultimate shear strength, 0.2% yield strength, shear modulus, and fracture strain were used as response variables to identify the most optimal build parameters. Additionally, stress-strain diagrams are presented to contrast elastic and plastic regions with traditional injection molding. Results demonstrated an interaction of factors in all mechanical measured variables whenever an orientation and a raster angle were applied. Compared to injection molding, FDM specimens were similar for all measured torsion variables except for the fracture strain; this led to the conclusion that the FDM process can fabricate components with similar elastic properties but with less ductility than injection molding. The orientation in YXZ with the raster angle at 00 resulted in the most suitable combination identified in the response optimization analysis. More... »

PAGES

307-317

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Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00170-018-1602-0

DOI

http://dx.doi.org/10.1007/s00170-018-1602-0

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https://app.dimensions.ai/details/publication/pub.1100553726


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