Chatter Diagnosis in Milling Using Supervised Learning and Topological Features Vector
Chatter Diagnosis in Milling Using Supervised Learning and Topological Features Vector
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DOI：
10.1109/icmla.2019.00200
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
Melih C. Yesilli;Sarah Tymochko;Firas A. Khasawneh;E. Munch
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文献类型：
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作者：
Melih C. Yesilli;Sarah Tymochko;Firas A. Khasawneh;E. Munch
由于颤振对刀具寿命、表面质量和机床主轴的影响，颤振检测已成为一个突出的研究课题。现有的颤振检测方法大多基于信号处理和信号分解。在这项研究中，我们使用模拟切削刀具振动的数据的拓扑特征，结合四种监督机器学习算法来诊断铣削过程中的颤振。持久性图是一种表示拓扑特征的方法，不容易在机器学习的上下文中使用，因此必须将其转换为更适合的形式。具体来说，我们将重点介绍两种不同的持久性图特征化方法，Carlsson坐标和模板函数。在本文中，我们提供了分类结果的模拟数据从各种切削配置，包括向上铣削和向下铣削，除了相同的数据与一些添加的噪声。我们的研究结果表明，卡尔森坐标和模板函数产生的准确度高达96%和95%，分别。我们还提供证据表明，这些拓扑方法是噪声鲁棒描述符颤振检测。
Chatter detection has become a prominent subject of interest due to its effect on cutting tool life, surface finish and spindle of machine tool. Most of the existing methods in chatter detection literature are based on signal processing and signal decomposition. In this study, we use topological features of data simulating cutting tool vibrations, combined with four supervised machine learning algorithms to diagnose chatter in the milling process. Persistence diagrams, a method of representing topological features, are not easily used in the context of machine learning, so they must be transformed into a form that is more amenable. Specifically, we will focus on two different methods for featurizing persistence diagrams, Carlsson coordinates and template functions. In this paper, we provide classification results for simulated data from various cutting configurations, including upmilling and downmilling, in addition to the same data with some added noise. Our results show that Carlsson Coordinates and Template Functions yield accuracies as high as 96% and 95%, respectively. We also provide evidence that these topological methods are noise robust descriptors for chatter detection.