In metal cutting, untended machining is being realized to increase the efficiency of working. As the unmanned automation comes true, the friction and abnormal state of machine tools are being closed up as important matters.
The method using AE(Acous...
In metal cutting, untended machining is being realized to increase the efficiency of working. As the unmanned automation comes true, the friction and abnormal state of machine tools are being closed up as important matters.
The method using AE(Acoustic Emission) has more merits like convenience in
handling, sensitivity in signaling, etc, than any other method. Therefore, the application to detecting the wear from machine tools has been tried and the method of detecting the value of AErms Volt has been made use of to study the wear of machine tools.
However, in high-speed cutting, occurence of chip breaking as well as wear of tools has become a important problem. Chip rupture is a crucial factor of manufacturing system's stability. In this study, AE signal has been detected and analyzed from chip rupture of turning process. Thus, at the result of experiments to detect the formation of chip by analyzing AE signals, the conclusion like the following are obtained, by way of adjusting the threshold value, it is possible separate and detect signals from continuous AE signals and burst AE signal. The count rate of burst AE signal is directly related with the formation of chip generated. It is possible to detect the chip formation by use of event count rate of burst AE signal.