| dc.description.abstract | Until today, milling process has been became one of the most commonly used processes in industry. Present industry requires not only productivity but quality of products. To the cutting process, an important factor that infects qualities, such as accuracy and surface characteristic, is vibration. To ease or even cancel the effect of vibration, we need an effective way to detect or process the vibration signal. Therefore, there is always an important issue that how we make a signal analyzing. Because this is the only way to help us to connect the real analog world with the digital world. However, most of these algorithms are forced to premise their targets are linear or stationary that may lose the information of what exactly happened in the real world.
Hilbert-Huang Transform (HHT) was proposed by Huang et al. in 1998, which is well at solving non-linear and non-stationary data due to its adaptation. Therefore, it has been used in widely used in acoustics, image processing, medical, meteorology and oceanography now. It mainly consists of two steps, empirical mode decomposition (EMD) and Hilbert transform (HT). Since this is a relatively new method and lack of theoretical proof, we need as possible as we can to prove its efficiency.
In this research, we used the HHT to analyze data which collected by strain gauge and accelerometer in end-milling process to investigate the applicability of HHT on machining process and, due to the experiment results, we had some conclusions as below:
1. The Margin spectrum which built by HHT had almost the same distribution as FFT spectrum in frequency domain, which means it could be used to get frequency domain data instead of using FFT.
2. Different with wavelet transform, HHT spectrum is able to present the change of vibration generated in cutting process in time-frequency domain and keeps the same resolution.
3. In a extra experiment, the combination of using strain gauge and HHT could also be useful to detect the tool wear. However, accelerometer data had a worse presentation than strain gauge was. The reason should be further investigated. | en_US |