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International Standard Serial Number:
ISSN 1001-4551
Sponsor:
Zhejiang University;
Zhejiang Machinery and Electrical Group
Edited by:
Editorial of Journal of Mechanical & Electrical Engineering
Chief Editor:
ZHAO Qun
Vice Chief Editor:
TANG ren-zhong,
LUO Xiang-yang
Tel:
86-571-87041360,87239525
Fax:
86-571-87239571
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No.9 Gaoguannong,Daxue Road,Hangzhou,China
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E-mail:
meem_contribute@163.com
Abstract: The tool condition during the shearing process of iron-based nanocrystalline alloy strips was critical to ensure the processing quality. To address the problem of tool wear monitoring during shear processing of ironbased nanocrystalline alloy strips, a shear tool wear online monitoring method based on acoustic emission signal was proposed. First, the acoustic emission monitoring equipment was built to determine the corresponding parameters, and the raw acoustic emission signal was collected for pre-processing to obtain the signal of shear processing stage for subsequent processing. Then, the relationship between shear tool wear and strip quality changes during the shear processing was studied, and according to the acoustic emission signal obtained during the shear processing, the time domain, frequency domain and time-frequency domain features were extracted, and the relationship between the obtained features and tool wear was analyzed. The features with good correlation were obtained by using ReliefF and principal component analysis (PCA) algorithms for feature selection and dimensionality reduction. Finally, based on the selected features, a support vector machines (SVM) artificial intelligence model was constructed to identify the shear tool wear stages. The results show that: with the increase of tool wear, the strip quality decreases, and there is a correspondence between the acoustic emission signal feature values and tool wear. Using ReliefF-PCA-SVM model can achieve 95.56% classification accuracy, which can effectively monitor tool wear during shear processing online.
Key words: acoustic emission monitoring equipment; iron-based nanocrystalline alloy; feature selection and dimensionality reduction; principal component analysis(PCA); support vector machines(SVM); ReliefF algorithm
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