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Design and Characterization of a Bi-Material Co-Extruder for Fused Deposition Modeling

[+] Author Affiliations
Mohammad Abu Hasan Khondoker, Dan Sameoto

University of Alberta, Edmonton, AB, Canada

Paper No. IMECE2016-65330, pp. V002T02A060; 9 pages
  • ASME 2016 International Mechanical Engineering Congress and Exposition
  • Volume 2: Advanced Manufacturing
  • Phoenix, Arizona, USA, November 11–17, 2016
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5052-7
  • Copyright © 2016 by ASME


Fused Deposition Modeling (FDM) is a popular style of additive manufacturing (AM) where a 3D object is fabricated by a molten material deposited into successive 2D layers. Multi-material 3D printing is particularly challenging due to poor adhesion between dissimilar plastics. The majority of currently available multi-material printers use separate nozzles for each material. Therefore, they are incapable of producing devices made of functionally gradient materials (FGM). FGM is a special class of engineering material exhibiting spatially inhomogeneous content, which tailors the material for specific functional and performance requirements. In this work, we present the design and characterization of a bi-material co-extruder system. This specialized extruder is capable of printing two thermoplastic materials through a single nozzle and is also capable of altering the composition of the deposited material while printing. It is also exceptional in that the internal structure of the hot end can be easily accessed and is a more adaptable design than previously reported co-extruders. The extruder was installed on a Geeetech PI3 Pro C printer. Then, some simple structures were printed using pairs of materials including polylactic acid (PLA), acrylonitrile butadiene styrene (ABS) and high impact polystyrene (HIPS). Without any internal mixing features, the composition of the output (for instance, different colored PLA) was determined from microscopic images of cross-section of the extrudates.

Copyright © 2016 by ASME
Topics: Design , Modeling



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