Tool Wear On-line Monitoring System Based On DART 750 Milling Machining Center

With the enhancement of manufacture automation and, especially, the appearance of flexible manufacture system (FMS), on-line monitoring of production processes has been receiving increased attention. In-process tool wear has a profound influence on the precision and roughness of work piece, and, even results in discarded product and interrupted machine. Tool failure, statistically, accounts for over 75% of facility faults. Hence, on-line supervision of tool condition has become an urgent requirement.Sign processing technology and pattern recognition are the two important sections. Wavelet transform is adept at analyzing unstationary signals with its time-frequency localization ability, and more and more attention has been paid on its applications in tool condition monitoring. Neural network can effectively realize nonlinear mapping from inputs to outputs, have very good performance of pattern recognition, and is viewed as a widely used and highly effective technique.In the paper,the object of research is the milling maching process on the Cincinnati DART750 machine.The paper particularly introduce signal processing technology including time-domain analysis,frequency domain analysis and wavelet analysis; we process the frequency band energy of the cutting force and vibration signal, and obtain the sensitive frequency band features of tool wear. Based on the changes of corresponding frequency band energy, we can monitor the condition of the tools effectively.Neural network was built to reflect the relationship between tool wear to cutting force signal and vibration signal, and finally, implement the recognition of tool wear. Meanwhile, the number of hidden layer, neuron and network training were also discussed.The paper gets the exercitation and test book of the cutting force and the vibration signal by experiment,and then to tests the validity by using the BP Network which is designed in the paper.The result shows that the experiment data can reflect the the tool wear,and the BP network also can rightly recognise the condition of tool wear.