Chatter identification in turning process based on vibration analysis using Hilbert-Huang transform
In machining process, the increasing cutting depth is aimed to accelerate machining operations in a short time. However, the increasing cutting depth can cause chatter vibration at any time. Chatter can accelerate tool wear and lead to poor machined surface. This paper studied the application of Hilbert-Huang transform (HHT) to analyse chatter caused by the increasing cutting depth during operation. Chatter vibration is typical of non-stationary and non-linear vibration signals, therefore it should be analysed by appropriate signal processing; HHT. In this research, initially “hammering tests” were conducted to observe dynamic modal parameters of cutting system; modal mass, damping ratio, and stiffness. Then stability lobe chart was generated based on those dynamic modal parameters to determine cutting parameters. Second, turning tests were conducted and then vibrations obtained in turning tests were analysed using HHT for chatter detection. The results were then compared by conventional signal processing; short-time Fourier (STFT) transforms. The results show that the empirical mode decomposition (EMD) of HHT process separated complex vibrations into simple components and isolated chatter from the others. The chatter was isolated in the first IMF. Therefore, chatter can be identified by EMD process. In the spectrum analysis, HHT spectra showed its superiority over STFT spectra. HHT spectra provided high time resolution and high frequency resolution both rather than STFT spectra that provided blurry and blocked spectra. The implication is that HHT can be applied to monitor the machining process.
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