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.

翻译：本研究探讨了监督式机器学习算法在预估熔融沉积成型工艺制成的聚乳酸试样极限抗拉强度中的应用。共制备31个PLA试样，以填充率、层高、打印速度和挤出温度作为输入参数。主要目标在于评估四种不同监督分类算法（即逻辑分类、梯度提升分类、决策树和K近邻）在预测试样UTS时的准确性与有效性。结果表明，虽然决策树与K近邻算法的F1分数均为0.71，但KNN算法的曲线下面积得分更高，达0.79，优于其他算法。这证明了KNN算法在区分数据集中两类极限抗拉强度方面具有更优异的能力，使其成为本研究背景下分类任务的最优选择。本研究首次尝试运用基于机器学习的分类算法预估PLA试样的UTS，所得结果为了解这些技术在增材制造领域提升预测模型性能与准确性方面的潜力提供了宝贵见解。