Research On The High-Speed CNC Integrated Manufacturing Health Monitoring Technology Based On Wavelet Analysis

With the development of high-speed processing technology, the problems in condition monitoring of manufacturing process are more and more prominent, and directly affects the efficiency of production. Therefore, to control the high-speed CNC integrated manufacturing process is to ensure product quality, increase processing efficiency, extend tool life and ensure the safety of personnel and equipment. The concept of structural health monitoring is introduced into high-speed CNC integrated manufacturing system. A number of conditions available information (such as cutting force, torque, power, spindle speed, vibration, thermal deformation, etc.) were used to describe the CNC machine tool processing. High-speed CNC integrated manufacturing health monitoring involves the monitoring of the spindle fever, ball screw fever monitoring, tool wear condition monitoring, work piece condition monitoring. This paper focuses on health monitoring method of the condition tool, and the proposed method is also appropriate with other aspects.In this paper, vibration sensors are used to collect the tool wear signal, and then the collected signals are processed and analyzed. By compared the different tool wear signals, features associated with the tool wear value are extracted. In time domain, the RMS signal is extracted as the feature value; in time-frequency domain, band wavelet packet energy monitoring method was adapt to analysis the frequency of vibration signals of energy statistics. Signal features were extracted as the feature value of the energy band. Then, fault diagnosis model of turning tool, which is based on BP neural network and D-S evidence theory, was established. This approach enriches and develops the tool wear monitoring technology. Finally, on the basis of these studies, a mixed VC Programming with MATLAB data processing system level is constructed.