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TECHNICAL PAPERS

Improved Inverse Solutions for On-Line Machine Tool Monitoring

[+] Author and Article Information
Lorraine Olson

Department of Mechanical Engineering

Robert Throne

Department of Electrical and Computer Engineering, Rose-Hulman Institute of Technology, Terre Haute, Indiana

Eric Rost

Department of Mechanical Engineering, University of Nebraska, Lincoln, Nebraska

J. Manuf. Sci. Eng 126(2), 311-316 (Jul 08, 2004) (6 pages) doi:10.1115/1.1688374 History: Received April 01, 2002; Revised December 01, 2003; Online July 08, 2004
Copyright © 2004 by ASME
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References

Yen,  D., and Wright,  P., 1986, “A Remote Temperature Sensing Technique for Estimating the Cutting Interface Temperature Distribution,” ASME J. Eng. Ind., 108, pp. 252–263.
Chow,  J., and Wright,  P., 1988, “On-Line Estimation of Tool/Chip Interface Temperatures for a Turning Operation,” ASME J. Heat Transfer, 110, pp. 56–64.
Xu,  W., Genin,  J., and Dong,  Q., 1997, “Inverse Method to Predict Temperature and Heat Flux Distribution in a Cutting Tool,” ASME J. Heat Transfer, 119, pp. 655–659.
Stephenson,  D., 1991, “An Inverse Method for Investigating Deformation Zone Temperatures in Metal Cutting,” ASME J. Eng. Ind., 113, pp. 129–136.
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Lin,  J., Lee,  S., and Weng,  C., 1992, “Estimation of Cutting Temperature in High Speed Machining,” ASME J. Eng. Mater. Technol., 114, pp. 289–296.
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Olson,  L., and Throne,  R., 2001, “Estimation of Tool/Chip Interface Temperatures for On-Line Tool Monitoring: An Inverse Problem Approach,” Inverse Probl. Eng., 9, pp. 367–388.
Throne,  R. D., and Olson,  L. G., 2000, “Fusion of Body Surface Potential and Body Surface Laplacian Signals for Electrocardiographic Imaging,” IEEE Trans. Biomed. Eng., 47, pp. 452–462.
Throne,  R. D., Olson,  L. G., and Hrabik,  T. J., 1999, “A Comparison of Higher-Order Generalized Eigensystem Techniques and Tikhonov Regularization for the Inverse Problem of Electrocardiography,” Inverse Probl. Eng., 7, pp. 143–193.
Throne,  R. D., Olson,  L. G., Hrabik,  T. J., and Windle,  J. R., 1997, “Generalized Eigensystem Techniques for the Inverse Problem of Electrocardiography Applied to a Realistic Heart-Torso Geometry,” IEEE Trans. Biomed. Eng., 44, pp. 447–454.
Olson,  L. G., Throne,  R. D., and Windle,  J. R., 1997, “Performance of Generalized Eigensystem and Truncated Singular Value Decomposition Methods for the Inverse Problem of Electrocardiography,” Inverse Probl. Eng., 5, pp. 239–277.
Olson,  L., and Throne,  R., 1995, “Computational Issues Arising in Multidimensional Elliptic Inverse Problems: The Inverse Problem of Electrocardiography,” Eng. Comput.,12(4), pp. 343–356.
Throne,  R. D., and Olson,  L. G., 1995, “The Effects of Errors in Assumed Conductivities and Geometry on Numerical Solutions to the Inverse Problem of Electrocardiography,” IEEE Trans. Biomed. Eng., 42, pp. 1192–1200.
Throne,  R., and Olson,  L., 1994, “A Generalized Eigensystem Approach to the Inverse Problem of Electrocardiography,” IEEE Trans. Biomed. Eng., 41, pp. 592–600.
“OMEGA Engineering Website: HFS Series Heat Flux Sensor,” http://www.omega.com, 2003.
Frolick,  G., 2001, “Thin Film Heat Flux Sensor,” Aerospace Technology Innovation,9(1), January/February.
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Stevenson,  M. G., Wright,  P. K., and Chow,  J. G., 1983, “Further Developments in Applying the Finite Element Method to the Calculation of Temperature Distributions in Machining and Comparisons With Experiment,” ASME J. Eng. Ind., 105, pp. 149–154.

Figures

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Two-dimensional model tool geometry. (All linear dimensions in mm.)
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Finite element mesh near tool insert showing 45 sensors for high conductivity 3 mm configuration
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Original and high conductivity tool temperature profiles on the sensor surface for Profile 1. (Solid Line: Original configuration, Dotted Line: High conductivity 3 mm configuration.)
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Original and high conductivity tool normalized temperature profiles on the sensor surface for Profile 1. (Solid Line: Original configuration, Dotted Line: High conductivity 3 mm configuration.)
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Finite element mesh near tool insert showing sensors for 1 mm tool configuration. (Circles: 32 sensors, Filled Circles: optimal 4 sensor subset.)
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Typical inverse solutions on the prediction surface for profile two. σ=5°C, four modes, four sensors. (Heavy solid line: true solution; thin solid line: Poly basis vectors; dashed line: GSVD basis vectors; dotted line: GESL basis vectors.)
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Typical inverse solutions on the prediction surface for profile two. σ=5°C, four modes, four sensors. (Heavy solid line: true solution; thin solid line: Poly basis vectors; dashed line: GSVD basis vectors; dotted line: GESL basis vectors.)

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