Transfer Learning for Autonomous Chatter Detection in Machining-猫眼课题宝

DOI：

10.1016/j.jmapro.2022.05.037

发表时间：

2022-04

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Melih C. Yesilli;Firas A. Khasawneh;B. Mann

大振幅颤振是加工过程中最重要的现象之一。它在切削操作中通常是有害的，导致表面光洁度差和刀具寿命缩短。因此，使用机器学习进行颤振检测在过去十年中一直是一个活跃的研究领域。在整个行业中应用机器学习进行颤振检测可以发现三个挑战：对不同流程中颤振特征的普遍性理解不够、自动化特征提取的需求以及每个特定工件-机床组合的数据有限（例如，在加工一次性产品时）。这三个挑战可以归为迁移学习的范畴，迁移学习涉及研究如何利用从一种环境中获得的知识来获取新环境中的信息。本文通过评估突出的以及新颖的颤振检测方法的迁移学习来研究自动颤振检测。我们使用从不同切削配置的车削和铣削实验中提取的各种特征来研究颤振分类的准确性。研究的方法包括快速傅立叶变换 (FFT)、功率谱密度 (PSD)、自相关函数 (ACF) 以及基于分解的工具，例如小波包变换 (WPT) 和集成经验模式分解 (EEMD)。我们还研究了基于拓扑数据分析（TDA）的最新方法和基于离散时间规整（DTW）的时间序列相似性度量。我们通过车削和铣削数据集内部和之间的培训和测试来评估每种方法的迁移学习潜力。探索了四种监督分类算法：支持向量机（SVM）、逻辑回归、随机森林分类和梯度提升。除了准确性之外，我们还评论了每种方法的特征提取的自动化潜力，这对于创建自主制造中心是不可或缺的。我们的结果表明，精心选择的时频特征可以带来较高的分类精度，尽管代价是需要手动预处理和专家用户的标记。另一方面，我们发现 TDA 和 DTW 方法可以提供与时频方法相当的精度和 F1 分数，而无需通过全自动管道进行手动预处理。此外，我们发现，当使用铣削数据进行训练并在车削数据上进行测试时，DTW 方法优于所有其他方法。因此，对于全自动颤振检测方案，TDA 和 DTW 方法可能优于基于时频的方法。当从有限的工件-机床组合或一次性过程的小数据集中汇集数据时，DTW 和 TDA 也更具优势。

Large-amplitude chatter vibrations are one of the most important phenomena in machining processes. It is often detrimental in cutting operations causing a poor surface finish and decreased tool life. Therefore, chatter detection using machine learning has been an active research area over the last decade. Three challenges can be identified in applying machine learning for chatter detection at large in industry: an insufficient understanding of the universality of chatter features across different processes, the need for automating feature extraction, and the existence of limited data for each specific workpiece-machine tool combination, e.g., when machining one-off products. These three challenges can be grouped under the umbrella of transfer learning, which is concerned with studying how knowledge gained from one setting can be leveraged to obtain information in new settings. This paper studies automating chatter detection by evaluating transfer learning of prominent as well as novel chatter detection methods. We investigate chatter classification accuracy using a variety of features extracted from turning and milling experiments with different cutting configurations. The studied methods include Fast Fourier Transform (FFT), Power Spectral Density (PSD), the Auto-correlation Function (ACF), and decomposition based tools such as Wavelet Packet Transform (WPT) and Ensemble Empirical Mode Decomposition (EEMD). We also examine more recent approaches based on Topological Data Analysis (TDA) and similarity measures of time series based on Discrete Time Warping (DTW). We evaluate transfer learning potential of each approach by training and testing both within and across the turning and milling data sets. Four supervised classification algorithms are explored: support vector machine (SVM), logistic regression, random forest classification, and gradient boosting. In addition to accuracy, we also comment on the automation potential of feature extraction for each approach which is integral to creating autonomous manufacturing centers. Our results show that carefully chosen time-frequency features can lead to high classification accuracies albeit at the cost of requiring manual pre-processing and the tagging of an expert user. On the other hand, we found that the TDA and DTW approaches can provide accuracies and F1-scores on par with the time-frequency methods without the need for manual preprocessing via completely automatic pipelines. Further, we discovered that the DTW approach outperforms all other methods when trained using the milling data and tested on the turning data. Therefore, TDA and DTW approaches may be preferred over the time-frequency-based approaches for fully automated chatter detection schemes. DTW and TDA also can be more advantageous when pooling data from either limited workpiece-machine tool combinations, or from small data sets of one-off processes.

Transfer Learning for Autonomous Chatter Detection in Machining

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