Issue |
MATEC Web Conf.
Volume 349, 2021
6th International Conference of Engineering Against Failure (ICEAF-VI 2021)
|
|
---|---|---|
Article Number | 01008 | |
Number of page(s) | 8 | |
Section | Composite Materials: Characterization, Mechanical Behavior and Modeling, Multifunctionality, Advanced Manufacturing Techniques | |
DOI | https://doi.org/10.1051/matecconf/202134901008 | |
Published online | 15 November 2021 |
Experimental investigation on flexural properties of FDM-processed PET-G specimen using response surface methodology
1 Laboratory of Manufacturing Processes & Machine Tools (LMProMaT), Department of Mechanical Engineering Educators, School of Pedagogical and Technological Education (ASPETE), Amarousion, GR 151 22, Greece
2 Manufacturing Technology Division, School of Mechanical Engineering, National Technical
University of Athens (NTUA), 9 Heroon Polytechniou Str., GR 157 80 Athens, Greece
3 Design & Manufacturing Lab., Department of FWSD, University of Thessaly, 11-13 V. Griva Str., Karditsa, GR 4130, Greece
* Corresponding author: vaxev@aspete.gr
The properties of fused deposition modeling (FDM) products exhibit strong dependence on process parameters which may be improved by setting suitable levels for parameters related to FDM. Anisotropic and brittle nature of 3D-printed components makes it essential to investigate the effect of FDM control parameters to different performance metrics related to resistance for improving strength of functional parts. In this work the flexural strength of polyethylene terephthalate glycol (PET-G) is examined under by altering the levels of different 3D-printing parameters such as layer height, infill density, deposition angle, printing speed and printing temperature. A response surface experiment was established having 27 experimental runs to obtain the results for flexural strength (MPa) and to further investigate the effect of each control parameter on the response by studying the results using statistical analysis. The experiments were conducted as per the ASTM D790 standard. The regression model generated for flexural strength adequately explains the variation of FDM control parameters on flexural strength and thus, it can be implemented to find optimal parameter settings with the use of either an intelligent algorithm, or neural network.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.