Detection of Tool Wear in Drilling CFRP/TC4 Stacks by Acoustic Emission

Abstract

Composite stacked material is widely used in many fields, such as aircraft, rocket, missile, and soon. In the process of drilling carbon fiber-reinforced polymer and titanium alloy lamination materials (CFRP/TC4, which is a typical structure in aircraft), tool wear is quick and serious, because the machining condition of the respective layers in stack is different. To ensure the drilling quality, drilling tool needs to be changed frequently. Therefore, if there is a detecting system in automatic drilling device to monitor and predict the tool wear effectively, it will help to improve the production efficiency and reduce the tool cost. Based on the experiments of drilling CFRP/TC4 stacks, the acoustic emission (AE) signals were carefully analyzed using the method of statistical analysis, spectrum analysis, and wavelet packet. The results show that the root-mean-square value of the AE signals and the energy of the wavelet packet are correlated with the tool wear. Meanwhile, experiments indicate that chips and tool fracture will cause instantaneous signal mutation, which may be appeared as disturbances. This has increased the difficulty of identifying the tool wear. With repeated experiments and comparison, it was found that the percentage of wavelet packet energy of some certain frequency sections increased with the tool wear, while some decreased. These variations will be possible to be used as features for further prediction in monitoring system.