TY - GEN
T1 - Influence of Filling Strategies on the Tensile Strength and Anisotropic Properties of Droplet-Based 3D-Printed Parts
AU - Struebig, Konstantin
AU - Diller, Felix
AU - Lueth, Tim C.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - In order to produce functional parts using additive manufacturing, it is crucial to know the mechanical properties resulting from the used manufacturing process and material. Since these properties depend on countless parameters, they have to be determined for each individual process. In this paper, an additive manufacturing process is investigated, that is based on the extrusion of droplets of molten polymer. While it is similar to the FFF process, this system uses standard granulate instead of special filaments and deposits individual droplets rather than continuous beads. Because of this, the droplet-based process is expected to show less anisotropic behavior than FFF. To test this, tension tests have been conducted with 3D-printed parts with different raster orientations and injection molded reference parts of the same material. The failure modes were investigated with a scanning electron microscope and the density of different specimens was compared. Lastly, the influence of the filling degree on the tensile strength was investigated for the default raster orientation. The results showed, that the anisotropic behavior of droplet-based parts is significantly less pronounced compared to FFF parts and exhibits different preferences. While FFF parts show the highest strength when the filaments are aligned parallel to the tension, this raster orientation yielded the worst results for the droplet-based approach. The highest scores were achieved here for a standard delta angle pattern, which only leads to average values for FFF. The filling degree proved to strongly influence the tensile strength of the tested specimens.
AB - In order to produce functional parts using additive manufacturing, it is crucial to know the mechanical properties resulting from the used manufacturing process and material. Since these properties depend on countless parameters, they have to be determined for each individual process. In this paper, an additive manufacturing process is investigated, that is based on the extrusion of droplets of molten polymer. While it is similar to the FFF process, this system uses standard granulate instead of special filaments and deposits individual droplets rather than continuous beads. Because of this, the droplet-based process is expected to show less anisotropic behavior than FFF. To test this, tension tests have been conducted with 3D-printed parts with different raster orientations and injection molded reference parts of the same material. The failure modes were investigated with a scanning electron microscope and the density of different specimens was compared. Lastly, the influence of the filling degree on the tensile strength was investigated for the default raster orientation. The results showed, that the anisotropic behavior of droplet-based parts is significantly less pronounced compared to FFF parts and exhibits different preferences. While FFF parts show the highest strength when the filaments are aligned parallel to the tension, this raster orientation yielded the worst results for the droplet-based approach. The highest scores were achieved here for a standard delta angle pattern, which only leads to average values for FFF. The filling degree proved to strongly influence the tensile strength of the tested specimens.
UR - https://www.scopus.com/pages/publications/85064117729
U2 - 10.1109/ROBIO.2018.8665190
DO - 10.1109/ROBIO.2018.8665190
M3 - Conference contribution
AN - SCOPUS:85064117729
T3 - 2018 IEEE International Conference on Robotics and Biomimetics, ROBIO 2018
SP - 14
EP - 20
BT - 2018 IEEE International Conference on Robotics and Biomimetics, ROBIO 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Robotics and Biomimetics, ROBIO 2018
Y2 - 12 December 2018 through 15 December 2018
ER -