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.

本研究探讨监督机器学习算法在估计使用熔丝沉积建模方法制备的聚乳酸试样的极限拉伸强度方面的应用，结果表明 KNN 算法在分类任务中表现最好，同时，该研究首次利用机器学习算法估计聚乳酸试样的 UTS，从而为附加制造领域中的预测模型性能和准确性提供了宝贵的信息。

应用机器学习辅助模式识别算法估算熔覆沉积建模聚乳酸样品的极限拉伸强度

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