Research On Warning Methods For Multi-characteristic Signals Of Tool Wear During Composite Laminate Structure Drilling

Carbon fiber-reinforced polymer(CFRP)and titanium alloy(TC4)lamination materials are widely used in aerospace manufacturing due to its excellent properties.However,different cutting performance of materials resulting in serious tool wear and make it difficult to guarantee production efficiency during composite laminate structure drilling.Traditional methods to predict tool wear condition are subjective,whose reliability is low and not suitable for automatic processing.With the rapid development of industrial automation,the need to realize online monitoring of tool wear is becoming more and more urgent.In this paper,the change of tool wear status during hole making process of CFRP/TC4 laminated structure is studied.The tool wear status is indirectly monitored and the residual life of the tool is predicted by monitoring the law of acoustic emission(AE)signal and power signal,which will provide the basis for tool changing.Firstly,set up and improve the acquisition platform of AE signal and power signal,which is synchronized and parallel.Secondly,analyse the signals by the method of time domain analysis,frequency domain analysis and time-frequency domain analysis,pick up the characteristic values of signals that are closely related to the tool wear.Research the relevance between signal characteristic values and hole quality combining with the tool flank wear measured by microscope.Then,using the signal characteristics closely related to the tool wear status establish the tool wear status monitoring and residual life prediction model which based on Hidden Markov Model(HMM).According to tool life,study the automatic tool changing strategy based on interruption type custom macro function of FANUC NC system.The results show that the root mean square value and the wavelet packet energy ratio of the main frequency band of AE signals,and the mean of power can indicate the tool wear status.The tool wear status monitoring and residual life prediction model can monitor current tool condition and predict future tool wear well.The interruption type custom macro function has a good effect in linking tool wear status monitoring model with automatic tool changing function of machine,which can improve the automation of equipment.