Abstract:
Fused deposition modeling is a modern rapid prototyping technique that is used for swiftly replicating concept modeling,
physical modeling, and end-of-line manufacture. Precision parameter selection is crucial for generating high-quality products
with excellent mechanical properties, such as tensile strength. This study looked at three essential process variables: infll
density, extruder temperature, and print speed. The relationship between these parameters and tensile strength of printed
polylactic acid components was investigated. Artifcial neural network (ANN) and Fuzzy logic (FL) method are utilized
to develop a prediction model. The test samples have been printed using a 3D forge Dreamer II FDM printing machine. In
Minitab software, the response surface design of the Box–Behnken technique with 15 experimental sets was used to organize
the trials. The results revealed that extruder temperature and print speed had a minor impact on tensile strength; however,
infll density has a large impact. The ANN and FL models all predicted tensile strength with a high degree of accuracy,
with maximum absolute percentage errors of 2.21%, and 3.29%, respectively. The model and the experimental data were
found to be in good agreement, according to the fndings. Furthermore, when compared to FL modeling, ANN models with
arithmetical value indices were the best predictive model.
