Research On Machine Cutting Tool Condition Monitoring Method Of Aeroplane Composite Material Processing

With the rapid development of China's aviation industry, the requirements of aircraft structural materials are increasing. Composite material are widely used in aircraft manufacturing because of its advantages of light weight, high strength, good process performance and so on. But composite material have structural characteristics of anisotropy and toughness strong, so it is difficult for machine to process. Its processing performance is poor, so the cutting process becomes very difficult. As a direct executor of composite materials process, cutting tool will bear different cutting resistance, so that it is more vulnerable to wear and tear, and that will affect the product quality and manufacturing costs.Therefore, online monitoring for the tool status in the processing of composite aircraft are urgent to ensure the safety of operation of modern aircraft manufacturing automation systems and the quality of the machine tool. The research has a promoting effect on solving the practical problems of domestic large aircraft manufacturing.Firstly, the research explores the mechanism of tool wear and analyzes three kind states of the tool wear, studys the causes and characteristics of acoustic emission signal. Secondly, the research designs aircraft composite materials processing machine tool condition monitoring system based on the industrial computer as control core and Matlab as software, and set up the hardware platform and software platform of the system. Thirdly, put acoustic emission signal in different tool wear condition as the object of the research for the time domain and frequency domain analysis to seek the characteristics of tool wear state. Then, in view of that aircraft composite materials processing machine tool condition monitoring is not easy to wear in the process of cutting, put forward the application of the wavelet packet energy method for aircraft composite machine tool condition monitoring. Finally, establish the BP neural network model of tool wear to solve the three states identification problem of tool wear. The computer simulation and actual test results verify the feasibility and effectiveness of the method.