www.gmachineinfo.com | 全球机械文献资源网

DATA-DRIVEN FAILURE PREDICTION IN BRITTLE MATERIALS: A PHASE FIELD-BASED MACHINE LEARNING FRAMEWORK
参考中译:数据驱动的脆性材料失效预测：一种基于相场的机器学习框架

刊名：	Journal of Machine Learning for Modeling and Computing
作者：	Eduardo A. Barros de Moraes（Department of Mechanical Engineering, Michigan State University） Hadi Salehi（Department of Mechanical Engineering, Michigan State University） Mohsen Zayernouri（Department of Mechanical Engineering, Michigan State University）
刊号：	739B0373
ISSN：	2689-3967
出版年：	2021
年卷期：	2021, vol.2, no.1
页码：	65-89
总页数：	25
分类号：	TP181; TP3
关键词：	Finite element method；Virtual sensing nodes；Pattern recognition；Artificial neural networks；K-nearest neighbor；Confusion matrix；Failure location/pattern
参考中译：	有限元方法；虚拟传感节点；模式识别；人工神经网络；K近邻；混淆矩阵；故障位置/模式
语种：	eng
文摘：	Failure in brittle materials led by the evolution of micro- to macro-cracks under repetitive or increasing loads is often catastrophic with no significant plasticity to advert the onset of fracture. Early failure detection with respective location are utterly important features in any practical application, both of which can be effectively addressed using artificial intelligence. In this paper, we develop a supervised machine learning (ML) framework to predict failure in an isothermal, linear elastic and isotropic phase-field model for damage and fatigue of brittle materials. Time-series data of the phase-field model is extracted from virtual sensing nodes at different locations of the geometry. A pattern recognition scheme is introduced to represent time-series data/sensor node responses as a pattern with a corresponding label, integrated with ML algorithms, used for damage classification with identified patterns. We perform an uncertainty analysis by superposing random noise to the time-series data to assess the robustness of the framework with noise-polluted data. Results indicate that the proposed framework is capable of predicting failure with acceptable accuracy even in the presence of high noise levels. The findings demonstrate satisfactory performance of the supervised ML framework and the applicability of artificial intelligence and ML to a practical engineering problem, i.e., data-driven failure prediction in brittle materials.
参考中译：	脆性材料在重复或不断增加的载荷下，由微裂纹到宏观裂纹的演化导致的破坏往往是灾难性的，没有明显的塑性来预示断裂的开始。具有各自位置的早期故障检测在任何实际应用中都是非常重要的功能，这两个功能都可以使用人工智能有效地解决。在本文中，我们发展了一个有监督的机器学习(ML)框架，用于预测脆性材料损伤和疲劳的等温、线弹性和各向同性相场模型中的失效。相场模型的时间序列数据是从几何上不同位置的虚拟传感节点提取的。提出了一种模式识别方案，将时序数据/传感器节点响应表示为具有相应标签的模式，并与ML算法相结合，用于识别模式的损伤分类。我们通过将随机噪声叠加到时间序列数据中进行不确定性分析，以评估该框架在噪声污染数据下的稳健性。结果表明，即使在高噪声水平下，所提出的框架也能够以可接受的精度预测故障。结果表明，有监督最大似然框架的性能令人满意，以及人工智能和最大似然方法在实际工程问题中的适用性，即脆性材料的数据驱动失效预测。

DATA-DRIVEN FAILURE PREDICTION IN BRITTLE MATERIALS: A PHASE FIELD-BASED MACHINE LEARNING FRAMEWORK参考中译:数据驱动的脆性材料失效预测：一种基于相场的机器学习框架

DATA-DRIVEN FAILURE PREDICTION IN BRITTLE MATERIALS: A PHASE FIELD-BASED MACHINE LEARNING FRAMEWORK
参考中译:数据驱动的脆性材料失效预测：一种基于相场的机器学习框架