Research Of Machine Tools State Mornitoring Based On Atificial Intelligence

In this thesis,the tool monitoring and control technology of both oversea and domestic has been studied,more focus on the application of the Artificial Neural Networks in the tool monitoring and control technology.Due to the extremely high non-linear mapping capability of Neural Networks,it is especially suitable for the identification of the complex pattern recognition,which has therefore become a widely used and effective measure for identifying the status of the tools.Hereinafter,the tool monitoring has been investigated based on the three main parts of the detecting system, i.e.signal detecting,character extract and pattern recognition.The main content of the thesis is as follows:·Analyse the development of tool condition monitoring and its status on native and overseas,illustrate research significance of this title.·Do research on tool wear monitoring and select indirect methods.Study all kinds of sensors adopted during tool machining.By comparing all sorts of detecting signals,the current signal of the motor was extracted.It has laid a solid foundation for successfully study the status of the tools.·Analyze the selected signals and extract the signal characters,analyzing the signals in the time domain,frequency domain and the time frequency domain.It has been raised that the wavelet theory should be adopted for analyzing the current signals.It enables extracting the signal characters of the tools effectively and provides the reliable reference for diagnosing the status of the tools.·Study BP net,RBF net and wavelet Neural Networks with the artificial intelligence networks and do simulation with Matlab,comparing the different networks which have been applied for the recognition of the status patterns of tools.At last,the recommendation and plan for improvement has been raised based on the conclusion of the thesis.