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Milling chatter detection by multi-feature fusion and Adaboost-SVM

机译：多尺寸融合和Adaboost-SVM铣削颤动检测

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摘要

Unstable chatter vibration in the milling process significantly affect the machining quality and efficiency. In order to suppress or avoid the chatter vibration in the cutting operation, detection of chatter onset is highly needed. Until now, most of the existing chatter detec-tion methods designed chatter indicators by extracting signal features, and the threshold of designed chatter indicator is usually needed, which is difficult to determine and might not be applicable in different cutting conditions. In fact, chatter detection is essentially a typ-ical classification problem, hence milling chatter detection based on machine learning method is presented in this paper. In order to obtain the needed data set, milling experi-ments under different cutting conditions were performed. Multi-features are utilized for the chatter detection, including the dimensionless features in time domain and frequency domain, and the automatic features extracted by stacked-denoising autoencoder (SDAE). In order to improve the accuracy of chatter classification and avoid the negative effects of possible samples with wrong labels, adaptive boosting (Adaboost) algorithm that consists of a series of weak classifiers by support vector machine (SVM) is utilized and further improved. Experimental verification and performance analysis are also performed, and the results show that the presented method can detect the chatter with a high accuracy and is applicable in different milling conditions.
机译：铣削过程中的不稳定颤动振动显着影响加工质量和效率。为了抑制或避免切割操作中的颤动振动，非常需要检测颤动发作。到目前为止，大多数现有的喋喋不休的撤销方法通过提取信号特征来设计颤抖动指示，并且通常需要设计的颤型指示器的阈值，这难以确定并且可能不适用于不同的切削条件。实际上，颤振检测基本上是一个典型的分类问题，因此本文提出了基于机器学习方法的铣削颤动检测。为了获得所需的数据集，执行在不同切削条件下的研磨实验。多个特征用于颤动检测，包括时域和频域中的无量纲特征，以及由堆积的自动化器（SDAE）提取的自动特征。为了提高颤振分类的准确性，避免使用错误标签的可能样本的负面影响，利用并进一步提高由支持向量机（SVM）组成的自适应升压（ADABoost）算法。还进行了实验验证和性能分析，结果表明，所提出的方法可以高精度地检测抗抖动，并适用于不同的研磨条件。

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  • 来源
    《Mechanical systems and signal processing》 |2021年第7期|107671.1-107671.15|共15页
  • 作者

    Shaoke Wan; Xiaohu Li; Yanjing Yin; Jun Hong;

  • 作者单位

    Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System Xi'an Jiaotong University Xi'an Shaanxi China School of Mechanical Engineering Xi'an Jiaotong University Xi'an Shaanxi China;

    Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System Xi'an Jiaotong University Xi'an Shaanxi China School of Mechanical Engineering Xi'an Jiaotong University Xi'an Shaanxi China;

    Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System Xi'an Jiaotong University Xi'an Shaanxi China School of Mechanical Engineering Xi'an Jiaotong University Xi'an Shaanxi China Luoyang Bearing Research Institute Co. Ltd. Luoyang Henan China;

    Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System Xi'an Jiaotong University Xi'an Shaanxi China School of Mechanical Engineering Xi'an Jiaotong University Xi'an Shaanxi China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Milling chatter detection; Multi-feature fusion; Strong classifier; Adaptive boosting; Support vector machine;

    机译：铣削颤动检测;多重特征融合;强分类器;自适应提升;支持矢量机器;

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