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Proceedings Paper

Detection of tool breakage in turning operations by using neural network
Author(s): Yongyue Zhang; Kunhua Zhang; Zhijun Han
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Paper Abstract

In this research, supervised and unsupervised neural network systems are used to detect tool breakage in turning operations. Before applying the neural network, the sensory signals of the cutting force are processed in time domain into more representative forms for the neural network to make the decision correctly. The back propagation (BP) network must be trained with samples of measurements taken at tool breakage. Utilizing feature mapping, the Kohonen's self-organizing network adapts the prototype values but cannot be used effectively on-line. Only relying on the normal category, the single category-based classifier (SCBC) adapts weight values on-line so as to continuously update the normal category. Extensive tests prove that the SCBC network correctly categorized 92% of the presented experimental data.

Paper Details

Date Published: 28 August 1995
PDF: 5 pages
Proc. SPIE 2620, International Conference on Intelligent Manufacturing, (28 August 1995); doi: 10.1117/12.217533
Show Author Affiliations
Yongyue Zhang, Tsinghua Univ. (China)
Kunhua Zhang, Tsinghua Univ. (China)
Zhijun Han, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 2620:
International Conference on Intelligent Manufacturing
Shuzi Yang; Ji Zhou; Cheng-Gang Li, Editor(s)

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