Experimental study on the 3D-printed plastic parts and predicting the mechanical properties using artificial neural networks


POLYMERS FOR ADVANCED TECHNOLOGIES, vol.28, no.8, pp.1044-1051, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 28 Issue: 8
  • Publication Date: 2017
  • Doi Number: 10.1002/pat.3960
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1044-1051
  • Keywords: Fused Deposition Modeling (FDM), tensile strength, artificial neural networks, LAYER ORIENTATION, OPTIMIZATION
  • Gazi University Affiliated: Yes


This study investigates the mechanical properties of 3D-printed plastic parts fabricated using Fused Deposition Modeling (FDM). For this purpose, a 3D printer named KASAME was designed and built by the researchers. The test samples were fabricated using polylactic acid (PLA). The experiments were conducted using three melt temperatures (190 degrees C, 205 degrees C, and 220 degrees C), four layer thickness values (0.06mm, 0.10mm, 0.19mm, and 0.35mm), and three raster pattern orientations (+45 degrees/-45 degrees [the crisscross pattern], horizontal and vertical). Tensile strength tests were performed to determine tensile strength values of the samples and fracture surfaces were also analyzed. Using artificial neural networks, a mathematical model for the tensile test results was generated corresponding to the raster pattern employed in 3D fabrication. Tensile strength tests indicated that melt temperature, layer thickness, and raster pattern orientation had a significant effect on the tensile strengths of the samples. According to the result of the experiment, the maximum average tensile strength values were observed for the samples fabricated using the crisscross raster pattern. The analysis of variance (ANOVA) table shows the raster pattern (PCR) value of 48.68% was obtained with the highest degree of influence. With respect to R-2, the best performing artificial neural network model, with test and training values of 0.999199 and 0.999997, respectively, was observed to be the crisscross raster pattern. Copyright (c) 2016 John Wiley & Sons, Ltd.