Impact of process parameters on improving the performance of 3D printed recycled polylactic acid (rPLA) components

The main goal of this research was to investigate the influence of additive manufacturing (AM) printing parameters on the mechanical properties and surface roughness of specimens fabricated using recycled polylactic acid (rPLA). In order to achieve this goal, significant printing parameters such as layer thickness, infill density, and nozzle temperature were selected based on prior research. A three-level L9 orthogonal array, based on the Taguchi method, was used in the experimental design. The mechanical properties of virgin PLA and recycled PLA printed specimens were examined and compared. To facilitate the analysis of variance (ANOVA) examination, the response data for mechanical and surface roughness parameters were transformed to signal-to-noise ( S/N ) ratios. The inspected responses under consideration were the surface roughness, shore D hardness, tensile strength, flexural strength, and impact strength. The main findings suggest that careful consideration of the layer height is crucial for achieving optimum mechanical properties in the recycled PLA specimens. Furthermore, the nozzle temperature also played an important factor that affected the mechanical and surface roughness properties of the 3D printed PLA specimens. Microscopic investigation demonstrated that the number and size of voids increased significantly when the layer thickness and temperature were low, namely, 0.1 mm and 195 ℃, respectively. Finally, the optimal combination of printing parameters for each performance characteristic was determined. Following this, a confirmation test was performed using the preferred combination of parameters, which indicated a strong correlation with the outcomes predicted statistically. The results obtained from this study revealed that recycled PLA exhibited mechanical properties comparable to that of virgin PLA under certain conditions. In summary, the results of this study will serve as a valuable dataset in the field of additive manufacturing, providing valuable insights for other researchers working with recycled PLA material.