Development of a Cutting Direction and Sensor Orientation Independent Monitoring Technique for End-Milling

1999 ◽  
Vol 122 (4) ◽  
pp. 671-677 ◽  
Author(s):  
John T. Roth ◽  
Sudhakar M. Pandit
Keyword(s):  
End Milling ◽  
Multivariate Models ◽  
Actual Event ◽  
Monitoring Technique ◽  
Sensor Orientation ◽  
Acceleration Data ◽  
Life Tests ◽  
Multivariate Autoregressive Models ◽  
Cutting Direction ◽  
Warning Time

In the authors’ previous work, univariate models were fit to acceleration data to predict impending tool failure. Numerous end-milling life tests, conducted under a wide variety of cutting conditions, demonstrated that the method could consistently warn of impending failure between 6 inches (15 cm) and 8 inches (20 cm) prior to the actual event. This paper presents an improved method that increases the warning time and allows the technique to function independent of the cutting direction or sensor orientation. Using multivariate autoregressive models fit to tri-axial accelerometer signals, monitoring indices are developed, verified and the results are compared with those from the univariate models. The multivariate models detected impending failure 30 inches (76 cm) prior to its occurrence, 23.5 inches (60 cm) earlier than with the univariate models. Furthermore, the multivariate models are able to monitor the condition of the tool, regardless of the cutting direction or sensor orientation. [S1087-1357(00)01003-0]

Development of a Cutting Direction and Sensor Orientation Independent Monitoring Technique for End-Milling

1999 ◽  
Author(s):  
John T. Roth ◽  
Sudhakar M. Pandit
Keyword(s):  
End Milling ◽  
Multivariate Models ◽  
Actual Event ◽  
Monitoring Technique ◽  
Sensor Orientation ◽  
Acceleration Data ◽  
Life Tests ◽  
Multivariate Autoregressive Models ◽  
Cutting Direction ◽  
Warning Time

Abstract In the authors’ previous work, univariate models were fit to acceleration data to predict impending tool failure. Numerous end-milling life tests, conducted under a wide variety of cutting conditions, demonstrated that the method could consistently warn of impending failure between 6 inches (15 cm) and 8 inches (20 cm) prior to the actual event. This paper presents an improved method that increases the warning time and allows the technique to function independent of the cutting direction or sensor orientation. Using multivariate autoregressive models fit to tri-axial accelerometer signals, monitoring indices are developed, verified and the results are compared with those from the univariate models. The multivariate models detected impending failure 30 inches (76 cm) prior to its occurrence, 23.5 inches (60 cm) earlier than with the univariate models. Furthermore, the multivariate models are able to monitor the condition of the tool, regardless of the cutting direction or sensor orientation.

Using the Eigenvalues of Multivariate Spectral Matrices to Achieve Cutting Direction and Sensor Orientation Independence

2005 ◽  
Vol 128 (1) ◽  
pp. 350-354 ◽  
Author(s):  
John T. Roth
Keyword(s):  
Cutting Tools ◽  
Autoregressive Models ◽  
Triaxial Accelerometer ◽  
Industrial Environment ◽  
Monitoring Techniques ◽  
Monitoring Technique ◽  
Sensor Orientation ◽  
Multivariate Autoregressive Models ◽  
Cutting Direction ◽  
Multivariate Autoregressive

There is a strong need for monitoring techniques capable of tracking the health of cutting tools under varying conditions. Unfortunately, most monitoring techniques are dependent on the cutting direction and/or the sensor orientation, limiting their effectiveness in the typical industrial environment. With this in mind, this research develops a monitoring technique that is independent of both of these factors. This is accomplished by using multivariate autoregressive models that are fit to the output from a triaxial accelerometer. The work shows that the eigenvalues of multivariate spectral matrices, calculated at the machining frequencies, are not only sensitive to the condition of the tool but are also independent of the direction of cutting and the orientation of the sensor. This independence is verified experimentally through tests conducted under a variety of cutting directions and sensor orientations.

Using the Eigenvalues of Multivariate Spectral Matrices to Monitor Equipment

2001 ◽  
Author(s):  
John T. Roth
Keyword(s):  
Cutting Tools ◽  
Autoregressive Models ◽  
Industrial Environment ◽  
Monitoring Techniques ◽  
Monitoring Technique ◽  
Sensor Orientation ◽  
Multivariate Autoregressive Models ◽  
Cutting Direction ◽  
Multivariate Autoregressive

Abstract There is a strong need in industry for monitoring techniques that are capable of tracking the health of cutting tools under varying conditions. Unfortunately, most monitoring techniques that are currently available are dependent on the cutting direction and/or the sensor orientation, limiting their effectiveness in the typical industrial environment. With this in mind, this research focuses on developing a monitoring technique that is independent of both of these factors. This is accomplished by using multivariate autoregressive models that are fit to the output from a tri-axial accelerometer. The work shows that the eigenvalues of multivariate spectral matrices, calculated at the machining frequencies, are sensitive to the condition of the tool. Furthermore, it is theoretically demonstrated that these eigenvalues are independent of the direction of cutting and the orientation of the sensor. This independence is verified experimentally through tests conducted under a variety of cutting directions and sensor orientations.

Monitoring End-Mill Wear and Predicting Tool Failure Using Accelerometers

1999 ◽  
Vol 121 (4) ◽  
pp. 559-567 ◽  
Author(s):  
J. T. Roth ◽  
S. M. Pandit
Keyword(s):  
Early Warning ◽  
End Milling ◽  
Cutting Conditions ◽  
Catastrophic Failure ◽  
End Mill ◽  
Systems Methodology ◽  
Tool Failure ◽  
Detection Scheme ◽  
Acceleration Data ◽  
Life Tests

Autoregressive models are fit to end-milling acceleration data and the Data Dependent Systems methodology is utilized to isolate the modal energies of the first and second multiples of the tooth pass frequency. The modal energies are shown to be closely linked to the wear curve and a detection scheme is developed that is capable of tracking the end-mill’s wear and providing an early warning of impending failure. Six life tests are conducted under varying conditions to demonstrate the capabilities of the detection scheme: standard cutting conditions, extreme cutting conditions, premature catastrophic failure and accelerometer placement. In all six cases, the detection scheme was able to provide a warning of impending failure several centimeters before the failure occurred.

Condition Monitoring With Round Ceramic Inserts While Face Milling Using Acceleration Data

2010 ◽  
Author(s):  
Justin L. Milner ◽  
John T. Roth
Keyword(s):  
Condition Monitoring ◽  
End Milling ◽  
Low Cost ◽  
Milling Operations ◽  
Tool Monitoring ◽  
Wear Life ◽  
Acceleration Data ◽  
Life Tests ◽  
The Fourier Transform ◽  
Machining Operations

In order to automate machining operations, it is necessary to develop robust tool condition monitoring techniques. In this paper, a tool monitoring strategy for round whisker-reinforced ceramic 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 machines capabilities, as a workpiece mounted dynamometer does. To demonstrate capabilities of the technique for round coated and uncoated ceramic tooling, six tool wear life tests were conducted under various conditions. The indirect method discussed herein successfully tracks the tool’s wear, even with the occurrence of minor chipping, 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.

scholarly journals Study on Ball End Milling of Cylindrical Surface : Influence of Cutting Direction on Machining Error

2005 ◽  
Vol 2005.3 (0) ◽  
pp. 933-938 ◽  
Author(s):  
Lin LU ◽  
Hisataka TANAKA ◽  
Masahiko SATO ◽  
Bernard W. IKUA ◽  
Yoshihito MAEDA ◽  
...  
Keyword(s):  
Cylindrical Surface ◽  
End Milling ◽  
Machining Error ◽  
Ball End Milling ◽  
Cutting Direction

End-Milling Machinability of Inconel 718

1996 ◽  
Vol 210 (1) ◽  
pp. 11-23 ◽  
Author(s):  
M Alauddin ◽  
M A El Baradie ◽  
M S J Hashmi
Keyword(s):  
Surface Roughness ◽  
Tool Life ◽  
Inconel 718 ◽  
End Milling ◽  
Milling Process ◽  
Machining Parameters ◽  
Life Tests ◽  
Life Values ◽  
Published Research ◽  
Carbide Inserts

Most published research works on machining Inconel 718 have been mainly concerned with turning, while the milling process has received little attention due to the complexity of the process. In this paper a series of end-milling experiments of Inconel 718 has been carried out in order to: (a) optimize cutting variables, (b) investigate tool life values and relationships and (c) investigate surface roughness. The machining parameters have been optimized by measuring cutting forces. Tool life tests have been carried out using carbide inserts and the surface roughness has been analysed.

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