Experimental investigation on flexural properties of FDM-processed PET-G specimen using response surface methodology

• Published in: MATEC Web of Conferences Vol. 349; p. 1008
• Main Authors: Fountas, Nikolaos A., Papantoniou, Ioannis, Kechagias, John D., Manolakos, Dimitrios E., Vaxevanidis, Nikolaos M.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 2021
• Subjects: Algorithms; Deposition; Flexural strength; Fused deposition modeling; Neural networks; Performance measurement; Polyethylene terephthalate; Process parameters; Regression models; Response surface methodology; Statistical analysis; Three dimensional printing
• ISSN: 2261-236X; 2274-7214; 2261-236X
• DOI: 10.1051/matecconf/202134901008

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.