用于估算熔融沉积成型聚乳酸试样极限抗拉强度的机器学习辅助模式识别算法
Machine Learning-Assisted Pattern Recognition Algorithms for Estimating Ultimate Tensile Strength in Fused Deposition Modeled Polylactic Acid Specimens
AI总结：
本文以FDM制备的31个PLA试样为对象，基于填充率、层高、速度和温度比较四种监督分类算法预测UTS，发现KNN以AUC 0.79表现最佳。
摘要：
在本研究中，我们研究了监督式机器学习算法在估算采用熔融沉积成型（FDM）工艺制造的聚乳酸（PLA）试样极限抗拉强度（UTS）方面的应用。共制备了31个PLA试样，并以填充百分比、层高、打印速度和挤出温度作为输入参数。主要目标是评估四种不同监督分类算法（即Logistic Classification、Gradient Boosting Classification、Decision Tree和K-Nearest Neighbor）在预测试样UTS方面的准确性和有效性。结果表明，尽管Decision Tree和K-Nearest Neighbor算法均取得了0.71的F1分数，但KNN算法表现出更高的曲线下面积（AUC）分数，为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的卓越能力，使其成为本研究背景下最有利的分类选择。本研究代表了首次尝试使用基于机器学习的分类算法估算PLA试样的UTS，研究结果为这些技术在增材制造领域提升预测模型性能和准确性的潜力提供了宝贵见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
