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Proceedings Paper

Neural networks based in process tool wear prediction system in milling wood operations
Author(s): Krzysztof Szwajka; Joanna Zielinska-Szwajka; Jaroslaw Gorski
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Paper Abstract

Neural networks in process tool wear prediction system has been proposed and evaluated in this study. A total of 100 experimental data have been received for training through a back-propagation neural networks model. The input variables for the proposed neural networks system were feed rate, cutting speed from the cutting parameters, and the force in the x,y-direction collected online using a dynamometer. After the proposed neural networks system had been established, two experimental testing cuts were conducted to evaluate the performance of the system. From the test results, it was evident that the system could predict the tool wear online with an average error of ±0.037 mm.

Paper Details

Date Published: 12 January 2009
PDF: 9 pages
Proc. SPIE 7133, Fifth International Symposium on Instrumentation Science and Technology, 713312 (12 January 2009); doi: 10.1117/12.812090
Show Author Affiliations
Krzysztof Szwajka, Univ. of Rzeszow (Poland)
Joanna Zielinska-Szwajka, Warsaw Agricultural Univ. (Poland)
Jaroslaw Gorski, Warsaw Agricultural Univ. (Poland)


Published in SPIE Proceedings Vol. 7133:
Fifth International Symposium on Instrumentation Science and Technology
Jiubin Tan; Xianfang Wen, Editor(s)

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