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In this study, we investigate the application of supervised machine learningalgorithms for estimating the Ultimate Tensile Strength (UTS) of PolylacticAcid (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 inputparameters. The primary objective was to assess the accuracy and effectivenessof four distinct supervised classification algorithms, namely LogisticClassification, Gradient Boosting Classification, Decision Tree, and K-NearestNeighbor, in predicting the UTS of the specimens. The results revealed thatwhile the Decision Tree and K-Nearest Neighbor algorithms both achieved an F1score of 0.71, the KNN algorithm exhibited a higher Area Under the Curve (AUC)score of 0.79, outperforming the other algorithms. This demonstrates thesuperior ability of the KNN algorithm in differentiating between the twoclasses of ultimate tensile strength within the dataset, rendering it the mostfavorable choice for classification in the context of this research. This studyrepresents the first attempt to estimate the UTS of PLA specimens using machinelearning-based classification algorithms, and the findings offer valuableinsights into the potential of these techniques in improving the performanceand accuracy of predictive models in the domain of additive manufacturing.

Machine Learning-Assisted Pattern Recognition Algorithms for Estimating  Ultimate Tensile Strength in Fused Deposition Modeled Polylactic Acid  Specimens