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CLC number: TH161.5
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
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Novel method for detection of anomalous structure characteristics of ID precision ultrathin monocrystalline silicon section cutting tool
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CHEN Zong-nong, GUO Ming, WANG Qing-jiu, YU Xue-gong. Novel method for detection of anomalous structure characteristics of ID precision ultrathin monocrystalline silicon section cutting tool[J]. Journal of Zhejiang University Science A, 2002, 3(3): 263-267.
@article{title="Novel method for detection of anomalous structure characteristics of ID precision ultrathin monocrystalline silicon section cutting tool",
author="CHEN Zong-nong, GUO Ming, WANG Qing-jiu, YU Xue-gong",
journal="Journal of Zhejiang University Science A",
volume="3",
number="3",
pages="263-267",
year="2002",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2002.0263"
}
%0 Journal Article
%T Novel method for detection of anomalous structure characteristics of ID precision ultrathin monocrystalline silicon section cutting tool
%A CHEN Zong-nong
%A GUO Ming
%A WANG Qing-jiu
%A YU Xue-gong
%J Journal of Zhejiang University SCIENCE A
%V 3
%N 3
%P 263-267
%@ 1869-1951
%D 2002
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2002.0263
TY - JOUR
T1 - Novel method for detection of anomalous structure characteristics of ID precision ultrathin monocrystalline silicon section cutting tool
A1 - CHEN Zong-nong
A1 - GUO Ming
A1 - WANG Qing-jiu
A1 - YU Xue-gong
J0 - Journal of Zhejiang University Science A
VL - 3
IS - 3
SP - 263
EP - 267
%@ 1869-1951
Y1 - 2002
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2002.0263
Abstract: The structure characteristics of ID precision ultrathin monocrystalline silicon section cutting machine-tool spindle with force-monitoring bearings functioning as force measuring sensors were detected with the new Hilbert theory based signal-wave envelope detection method, presented to replace the conventional hardware device in order to ensure that the signal is measured online with high fidelity. According to the probability of anomalous incidents in the cutting process, a mathematical recognition model has been designed and verified on an STC-22ID machine.
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