CONDITION MONITORING FOR INDEXABLE CARBIDE END MILL USING ACCELERATION DATA

In order to automate machining operations, it is necessary to develop robust tool condition monitoring techniques. In this paper, a tool monitoring strategy for indexable tungsten carbide end milling tools is presented based on the Fourier transform and statistical analysis of the vibrations of the tool during the machining operations. Using a low-cost, tri-axial piezoelectric accelerometer, the presented algorithm demonstrates the ability to accurately monitor the condition of the tools as the wear increases during linear milling operations. One benefit of using accelerometer signals to monitor the cutting process is that the sensor does not limit the machine's capabilities, as a workpiece mounted dynamometer does. To demonstrate capabilities of the technique, four tool wear life tests were conducted under various conditions. The indirect method discussed herein successfully tracks the tool's wear and is shown to be sensitive enough to provide sufficient time to replace the insert prior to damage of the machine tool, cutter, and/or workpiece.

[1]  XiaoQi Chen,et al.  An experimental study of tool wear and cutting force variation in the end milling of Inconel 718 with coated carbide inserts , 2006 .

[2]  D. E. Dimla,et al.  On-line metal cutting tool condition monitoring.: I: force and vibration analyses , 2000 .

[3]  Christopher A. Suprock,et al.  Directionally Independent Failure Prediction of End-Milling Tools During Pocketing Maneuvers , 2007 .

[4]  Christopher A. Suprock,et al.  Directionally Independent Failure Prediction of End-Milling Tools by Tracking Increasing Chaotic Noise at the Machining Frequencies Due to Wear , 2008 .

[5]  Shih-Chieh Lin,et al.  Tool wear monitoring in face milling using force signals , 1996 .

[6]  Adam G. Rehorn,et al.  State-of-the-art methods and results in tool condition monitoring: a review , 2005 .

[7]  Imtiaz Ahmed Choudhury,et al.  Wear mechanism of TiN coated carbide and uncoated cermets tools at high cutting speed applications , 2004 .

[8]  Bernhard Sick,et al.  ON-LINE AND INDIRECT TOOL WEAR MONITORING IN TURNING WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW OF MORE THAN A DECADE OF RESEARCH , 2002 .

[9]  Christopher A. Suprock,et al.  Endmill Condition Monitoring and Failure Forecasting Method for Curvilinear Cuts of Non-Constant Radii , 2007 .

[10]  Sang Jo Lee,et al.  Development of in situ system to monitor the machining process using a piezo load cell , 2005 .

[11]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[12]  Christopher A. Suprock,et al.  Endmill Condition Monitoring and Failure Forecasting Method for Curvilinear Cuts of Nonconstant Radii , 2009 .

[13]  Joseph C. Chen,et al.  An artificial-neural-networks-based in-process tool wear prediction system in milling operations , 2005 .

[14]  Gino Dini,et al.  Tool condition monitoring in end milling using a torque-based sensorized toolholder , 2007 .

[15]  Stephen C. Veldhuis,et al.  Mechanistic Modeling of Ball End Milling Including Tool Wear , 2006 .

[16]  M. Petró‐Turza,et al.  The International Organization for Standardization. , 2003 .

[17]  Joseph C. Chen,et al.  Tool condition monitoring in an end-milling operation based on the vibration signal collected through a microcontroller-based data acquisition system , 2008 .

[18]  Laine Mears,et al.  Quality and Inspection of Machining Operations: Review of Condition Monitoring and CMM Inspection Techniques — 2000 to Present , 2007 .

[19]  S. Sharif,et al.  Cutting performance and wear characteristics of PVD coated and uncoated carbide tools in face milling Inconel 718 aerospace alloy , 2001 .

[20]  Ibrahim N. Tansel,et al.  Detection of tool failure in end milling with wavelet transformations and neural networks (WT-NN) , 1995 .

[22]  Steven Y. Liang,et al.  Machining Process Monitoring and Control: The State-of-the-Art , 2004 .