Fractal Estimation of Flank Wear in Turning

[+] Author and Article Information
Satish T. S. Bukkapatnam

University of Southern California, Los Angeles, CA 90089

Soundar R. T. Kumara, Akhlesh Lakhtakia

Pennsylvania State University, University Park, PA 16802

J. Dyn. Sys., Meas., Control 122(1), 89-94 (Jun 04, 1999) (6 pages) doi:10.1115/1.482446 History: Received June 04, 1999
Copyright © 2000 by ASME
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Poincaré section plots of force and vibration signals—collected at cutting speed V=130 ft./min, feed f=0.0088 in./rev—capturing the variations in machining dynamics with flank wear. Fresh, partially worn and fully worn refer, respectively, to hw=0.000, 0.0060, and 0.0175 in.
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Flow chart showing the battery of tests for identification and characterization of underlying process dynamics
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Methodology of fractal estimation consisting of an offline training phase and an online estimation phase
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Design of experiments: Solid and dashed outlines of circle indicate whether that the exemplar patterns corresponding to that design point are used for training or testing
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Representative plots showing (a) the variation of average fractal dimension with dE for two experimental runs conducted using a fresh tool: V=160 ft./min,f=0.0136 in./rev. (b) the effect of signal separation on the fractal dimension estimates: — corresponds to the separated signal, and –⋅–⋅–⋅– corresponds to the nonseparated signal. As a result of signal separation, the change of slope of the graph becomes more pronounced. This reduces the uncertainty in deciding the linear portion of the graph thus rendering the computation of the slope of the linear portion of the graph, and hence the fractal dimension estimates more accurate.
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Architecture of the recurrent neural network
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Influence of time of cutting, and hence the extent of flank wear, on the estimation error
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(a) Comparison of interpolates of the flank wear estimates (– – –) and the actual measured flank wear (—). The experiment was conducted at V=160 ft./min and f=0.0136 in./rev. (b) An empirical q–q plot showing the equivalence of the distributions of the estimation errors corresponding to the exemplar patterns used for training the neural network, and those corresponding to the testing patterns.




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