| Micro milling technology has developed rapidly in recent years, and gradually becomes the important means of micro machining for the following advantages: the high machining accuracy,three dimensional processing ability and wide application scope of workpiece materials. Micro milling is a material removal process in micro scale range. Small changes in the tool wear state directly led to the problems as decreasing the workpiece processing quality, increasing production cost etc. Tool condition monitoring technology(TCM) has important significance to improving product quality, production efficiency & reducing the cost of production. In recent years, acoustic emission(AE) detecting technology has been more and more applied in tool condition monitoring as a kind of dynamic nondestructive detecting technology. This paper mainly acquires AE signals through micro milling acoustic emission detecting platform to carry out research on AE signal characteristics of tool wear state. The main work and achievements are as follows:(1) Study on basic theory of AE detecting technology and summarize the commonly signal analysis method in time domain analysis, frequency domain analysis & time-frequency domain analysis. The wavelet packet decomposition band disorder phenomenon is pointed out and programs are written by MATLAB to achieve related signal processing functions.(2) Build up the micro milling acoustic emission detecting platform which can simultaneously sample the AE signal & the cutting force signal. Tests were carried out for the detecting platform system to prepare for the follow-up experiments. Analysis indicates that the environmental noise mainly influence the AE signal in low frequency band.(3) The single factor micro milling experiments of TU1 were conducted by PCD micro milling tools. Contrast cutting force signal and AE signal acquired during the experiments, results are found out: the cutting force and AE signal increases with the increase of cutting parameters(cutting speed,feed per tooth, depth of cut); more intense the cutting edge radius size effect is, greater the AE signal average power is.(4) The micro milling tool wear experiments of TU1 & TC4 were conducted by PCD micro milling tools. Cutting edge radius was measured as the amount of tool wear. AE signals under different tool wear state and tool wear mechanism of micro milling are statistically analyzed in time domain, frequency domain(power spectrum analysis) and wavelet analysis. The results show that the AE signal average power and ring-down count are closely related to tool wear state; more severe thetool wear is, stronger the high frequency AE signal(around 750 ~ 1000 kHz) is; the feature vector of acoustic emission signal is extracted to represent the tool wear state. |
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