A novel intelligent control architecture has been developed to regulate cutting forces during single-point turning operations. A self-adapting Sliding Mode Controller (SMC) accounts for parameter variations and unmodeled dynamics in the cutting process. A unique artificial neural network, the 2-Sigma network, statistically bounds modeling uncertainties between a low-order, linear dynamic model and the actual cutting process. These uncertainty bounds provide “localized” gains for the SMC, thus reducing excess control activity while maintaining performance. Initially, the 2-Sigma networks are trained off-line using experimental data from a variety of operating conditions. In the final implementation, the 2-Sigma networks are updated on-line, allowing the SMC to respond to parameter variations and unmodeled dynamics. Experiments conducted on a CNC lathe demonstrate the exceptional performance and robustness of this control system.
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Intelligent Sliding Mode Control of Cutting Force During Single-Point Turning Operations
Gregory D. Buckner, Mem. ASME Assistant Professor,
Gregory D. Buckner, Mem. ASME Assistant Professor,
Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 27695
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Gregory D. Buckner, Mem. ASME Assistant Professor,
Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 27695
Contributed by the Manufacturing Engineering Division for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received January 2000; revised July 2000. Associate Editor: M. A. Elbestawi.
J. Manuf. Sci. Eng. May 2001, 123(2): 206-213 (8 pages)
Published Online: July 1, 2000
Article history
Received:
January 1, 2000
Revised:
July 1, 2000
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Buckner, G. D. (July 1, 2000). "Intelligent Sliding Mode Control of Cutting Force During Single-Point Turning Operations ." ASME. J. Manuf. Sci. Eng. May 2001; 123(2): 206–213. https://doi.org/10.1115/1.1366683
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