As a kind of self-excited vibrations, chatter vibration is extremely common in end milling, especially in high-speed cutting processes. It affects the machining accuracy of products and decreases the processing efficiency of machine tools. Thus it is very crucial to develop an effective condition monitoring system to extract the chatter feature before chatter vibration grows. In this paper, a hybrid chatter detection method (HCDM) is proposed for chatter feature extraction and classification in end milling. Firstly, wavelet packet decomposition is employed to decompose cutting vibration signals into a series of wavelet coefficients, and the signals of each frequency band are reconstructed. Secondly, fast Fourier transform and singular spectrum analysis are chosen to obtain the chatter features. Furthermore, the support vector machine model is optimized by particle swarm optimization to recognize the cutting states in end milling. At last, cutting experiments of 300 M steel under different machining conditions are conducted, and the results indicate that the proposed HCDM can distinguish the stable, transition, and chatter states accurately and rapidly in end milling.

中文翻译：

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基于WPD，SSA和SVM-PSO的混合抖动检测方法

作为一种自激振动，颤动振动在立铣刀中非常普遍，尤其是在高速切削过程中。它会影响产品的加工精度，并降低机床的加工效率。因此，开发一种有效的状态监视系统以在颤动振动增大之前提取颤动特征非常关键。本文提出了一种混合颤振检测方法（HCDM），用于端铣削中的颤振特征提取和分类。首先，利用小波包分解将切削振动信号分解为一系列的小波系数，并重构各个频带的信号。其次，选择快速傅立叶变换和奇异频谱分析以获得颤动特征。此外，支持向量机模型通过粒子群优化进行优化，以识别立铣中的切削状态。最后，在不同的加工条件下进行了300 M钢的切削试验，结果表明，所提出的HCDM可以在端铣削中准确，快速地识别出稳定，过渡和颤动状态。