Tool breakage detection in CNC high-speed milling based in feed-motor current signals

Abstract

Tool condition monitoring, mainly tool breakage detection for high-speed machining (HSM), is an important problem to solve; however, the techniques or types of sensors applied in other research projects present certain inconveniences. In order to improve tool breakage monitoring systems, a simple, effective, and fast method is presented herein. This method is based on the discrete wavelet transform (DWT) and statistical methodologies. The effectiveness of the method is based on the measurements of the feed-motor current signals using inexpensive sensors. It is well-known that during the cutting process, the motor current is related to the tool condition. The current consumption changes when the tool is broken as compared to when the tool is in normal cutting condition. This difference can be obtained from the waveform variances between the signals in order to ascertain the tool condition. The algorithms of this research project consist of obtaining compressed signals from the Irms feed-motor current signals applying the DWT. Then from these compressed signals, we detect the asymmetries between them. The arithmetic mean value is applied to asymmetries of consecutive machining lengths to reduce noise in the data having a mean value of a series of asymmetries; also, a normal cutting threshold is set up in order to make decisions regarding the tool conditions so as to detect tool breakage. Therefore, this research project shows a low-cost monitoring system that is simple to implement.