Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication

M. Saleh Shaikh Mohammad Meiabadi, Mahmoud Moradi*, Mojtaba Karamimoghadam, Sina Ardabili, Mahdi Bodaghi, Manouchehr Shokri, Amir Hossein Mosavi*

*Corresponding author for this work

Research output: Contribution to JournalArticlepeer-review


Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the
end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA
as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5% for toughness, part thickness, and production cost, respectively, in comparison
with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects.
Original languageEnglish
Article number3219
Issue number19
Early online date23 Sep 2021
Publication statusPublished - 23 Sep 2021
Externally publishedYes


  • fused filament fabrication
  • toughness
  • 3D printing
  • machine learning
  • Deep learning
  • artificial intelligence
  • computational mechanics
  • materials design
  • Big Data
  • data science


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