215 / 2019-04-06 19:49:42
PREDICTION OF THE HARDNESS OF X12M USING BARKHAUSEN NOISE AND CHEBYSHEV POLYNOMIALS REGRESSION METHODS
Non-destructive Evaluation,Hardness Prediction,Chebyshev polynomials regression,Barkhausen Noise
全文待审
Li Zibo / Beijing JingHang Research institute of computation and communication, The Classified Information Carrier Safety Management Engineering Technology Research Center of Beijing, Faculty of Information Tec
Li Shicheng / Beijing JingHang Research institute of computation and communication, The Classified Information Carrier Safety Management Engineering Technology Research Center of Beijingn,
Wang Donghao / Beijing JingHang Research institute of computation and communication, The Classified Information Carrier Safety Management Engineering Technology Research Center of Beijing
Sun Guangmin / Faculty of Information Technology, Beijing University of Technology
Li Yu / Faculty of Information Technology, Beijing University of Technology
He Cunfu / College of Mechanical Engineering and Applied Electronics Technology
Liu Xiucheng / College of Mechanical Engineering and Applied Electronics Technology
Cai Yanchao / College of Mechanical Engineering and Applied Electronics Technology
Wang Chu / Beijing JingHang Research institute of computation and communication, The Classified Information Carrier Safety Management Engineering Technology Research Center of Beijingn,
Barkhausen noise (BN) is an efficient non-destructive evaluation signal that could be used to predict the properties of materials such as hardness, residual stress and carbon content. Current BN signal analysis methods failed to describe the highly variated BN signal and achieved the limited regression accuracy. In this paper, two multi-variable regression tools was firstly employed to predict the property of material. Combined with Chebyshev polynomials, our regression tools were designed on the basis of cascaded regression and mutual-information-based feature selection. As implied by the experimental results on predicting the hardness of Cr12MoV material (i.e. X12m), our proposed method outperforms other comparative methods including neural network and partial linear square regression method.
重要日期
  • 会议日期

    09月11日

    2019

    09月14日

    2019

  • 09月14日 2019

    注册截止日期

  • 11月30日 2019

    初稿截稿日期

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