Machine Learning-Assisted Pattern Recognition Algorithms for Estimating
  Ultimate Tensile Strength in Fused Deposition Modeled Polylactic Acid
  Specimens
Akshansh Mishra Vijaykumar S Jatti
In this study, we investigate the application of supervised machine learning
algorithms for estimating the Ultimate Tensile Strength (UTS) of Polylactic
Acid (PLA) specimens fabricated using the Fused Deposition Modeling (FDM)
process. A total of 31 PLA specimens were prepared, with Infill Percentage,
Layer Height, Print Speed, and Extrusion Temperature serving as input
parameters. The primary objective was to assess the accuracy and effectiveness
of four distinct supervised classification algorithms, namely Logistic
Classification, Gradient Boosting Classification, Decision Tree, and K-Nearest
Neighbor, in predicting the UTS of the specimens. The results revealed that
while the Decision Tree and K-Nearest Neighbor algorithms both achieved an F1
score of 0.71, the KNN algorithm exhibited a higher Area Under the Curve (AUC)
score of 0.79, outperforming the other algorithms. This demonstrates the
superior ability of the KNN algorithm in differentiating between the two
classes of ultimate tensile strength within the dataset, rendering it the most
favorable choice for classification in the context of this research. This study
represents the first attempt to estimate the UTS of PLA specimens using machine
learning-based classification algorithms, and the findings offer valuable
insights into the potential of these techniques in improving the performance
and accuracy of predictive models in the domain of additive manufacturing.