Abstract
Based on the signal data acquired in drilling process of carbon fiber reinforced polymer/titanium alloy (CFRP/Ti) stacked materials, the acoustic emission (AE) characteristic values were carefully studied, by using the method of statistical analysis, spectrum analysis and wavelet packet. The results show that the root mean square(RMS) value of the AE signals and the energy of the wavelet packet are closely related to the tool wear. Meanwhile, experiments indicate that different materials, chips and tool tipping will cause instantaneous signal mutation, which has different forms in time domain and in time-frequency domain. These mutations may increase the difficulty of identifying the tool wear. Fortunately, with repeated experiments and comparison, some identifiable mutations were recognized. When a tool is processed from CFRP to Ti, the signal intensity decreases generally, the high-frequency component of signal increases gradually, and the signal has a tendency to show in high frequencies.
Highlights
Composite stacked material is widely used in many fileds, such as aircraft, rocket, missile and so on
1)In the experiments of high speed steel drill, we find that the signal energy is mainly concentrated from 125KHz to 250 KHz frequency range
The signal's root mean square (RMS) value produced by a high speed steel tool is at a high level in the initial stage, which indicated that the signal has a strong energy at the beginning
Summary
Composite stacked material is widely used in many fileds, such as aircraft, rocket, missile and so on. In order to improve the positional accuracy of the assembly hole, the method of one-off drilling the stacked material is widely applied to the machining of holes. Researchers at home and abroad have applied AE method to tool wear monitoring such as turning, milling and drilling. From their studies, some effective results have been obtained. K Jemielniak had studied that the kurtosis and energy of AE signal are sensitive to the tool wear condition, [1]. In Xie Jianfeng’s research, the AE signal [31.25250]KHz frequency energy ratio was used as the feature of tool wear monitoring,[3]. Hu Jianglin studied that the speed of tool wear can be expressed clearly by the number of AE signal ringing,[6]
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