Complexity measure of motor current signals for tool flute breakage detection in end milling

Automated tool condition monitoring is an important issue in the advanced machining process. Permutation entropy of a time series is a simple, robust and extremely fast complexity measure method for distinguishing the different conditions of a physical system. In this study, the permutation entropy of feed-motor current signals in end milling was applied to detect tool breakage. The detection method is composed of the estimation of permutation entropy and wavelet-based de-noising. To confirm the effectiveness and robustness of the method, typical experiments have been performed from the cutter runout and entry/exit cuts to cutting parameters variation. Results showed that the new method could successfully extract significant signature from the feed-motor current signals to effectively detect tool flute breakage during end milling. Whilst, this detection method was based on current sensors, so it possesses excellent potential for practical and real-time application at a low cost by comparison with the alternative sensors.

[1]  Yusuf Altintas,et al.  Prediction of Cutting Forces and Tool Breakage in Milling from Feed Drive Current Measurements , 1992 .

[2]  Paul William Prickett,et al.  An overview of approaches to end milling tool monitoring , 1999 .

[3]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[4]  Chen Ming,et al.  On-line tool breakage monitoring in turning , 2003 .

[5]  Xiaoli Li,et al.  Dynamical characteristics of pre-epileptic seizures in rats with recurrence quantification analysis , 2004 .

[6]  A. M. Bassiuny,et al.  Flute breakage detection during end milling using Hilbert–Huang transform and smoothed nonlinear energy operator , 2007 .

[7]  Yusuf Altintas,et al.  In-Process Detection of Tool Failure in Milling Using Cutting Force Models , 1989 .

[8]  C. W. de Silva,et al.  Tool wear detection and fault diagnosis based on cutting force monitoring , 2007 .

[9]  Ming Liang,et al.  Mechanical fault detection using fuzzy index fusion , 2007 .

[10]  Xiaoli Li,et al.  Current-sensor-based feed cutting force intelligent estimation and tool wear condition monitoring , 2000, IEEE Trans. Ind. Electron..

[11]  Kantz.  Quantifying the closeness of fractal measures. , 1994, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[12]  Rene de Jesus Romero-Troncoso,et al.  Sensorless tool failure monitoring system for drilling machines , 2006 .

[13]  Jongwon Kim,et al.  Real-Time Tool Breakage Monitoring for NC Milling Process , 1995 .

[14]  L M Hively,et al.  Detecting dynamical changes in time series using the permutation entropy. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Yusuf Altintas,et al.  In-process detection of tool breakages using time series monitoring of cutting forces , 1988 .

[16]  Xiaoli Li,et al.  Detection of tool flute breakage in end milling using feed-motor current signatures , 2001 .

[17]  C. Bandt.  Ordinal time series analysis , 2005 .

[18]  Soundarr T. Kumara,et al.  Flank Wear Estimation in Turning Through Wavelet Representation of Acoustic Emission Signals , 2000 .

[19]  D. Hutton,et al.  Acoustic Emission Monitoring of Tool Wear in End-Milling Using Time-Domain Averaging , 1999 .

[20]  Ibrahim N. Tansel,et al.  Acoustic Emission-Based Tool-Breakage Detector (TBD) for Micro-End-Milling Operations , 2001 .

[21]  Mathieu Ritou,et al.  A new versatile in-process monitoring system for milling , 2006, 1309.3915.

[22]  D. Dornfeld,et al.  In-Process Tool Fracture Detection , 1984 .

[23]  Thomas Schreiber,et al.  Detecting and Analyzing Nonstationarity in a Time Series Using Nonlinear Cross Predictions , 1997, chao-dyn/9909044.

[24]  Hiroyasu Iwabe,et al.  Effect of tool stiffness upon tool wear in high spindle speed milling using small ball end mill , 2001 .

[25]  Masayoshi Tomizuka,et al.  On-Line Monitoring of Tool and Cutting Conditions in Milling , 1989 .

[26]  Yuan Zhejun,et al.  Tool wear monitoring with wavelet packet transform—fuzzy clustering method , 1998 .