论文标题
在特征空间中的机器学习模型以群集和映射互动腐败制度
Machine learning model to cluster and map tribocorrosion regimes in feature space
论文作者
论文摘要
Tribocorsosion地图的目的是确定可接受的降解率的工作条件。本文提出了一种基于机器学习的方法来生成摩擦式图像，该方法可用于预测Tribosystem的性能。首先，无监督的机器学习用于识别和标记互动实验数据的簇。然后使用确定的簇训练支持向量分类模型。训练有素的SVM用于生成二聚态腐蚀图。将生成的地图与文献的标准图进行了比较。
Tribocorrosion maps serve the purpose of identifying operating conditions for acceptable rate of degradation. This paper proposes a machine learning based approach to generate tribocorrosion maps, which can be used to predict tribosystem performance. First, unsupervised machine learning is used to identify and label clusters from tribocorrosion experimental data. The identified clusters are then used to train a support vector classification model. The trained SVM is used to generate tribocorrosion maps. The generated maps are compared with the standard maps from literature.
