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[会议论文]

Prediction of the Hardness of X12m Using Barkhausen Noise and Chebyshev Polynomials Regression Methods

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Author:

Li, Zibo (Li, Zibo.) | Li, Shicheng (Li, Shicheng.) | Wang, Donghao (Wang, Donghao.) | Unfold

Indexed by:

EI Scopus

Abstract:

Barkhausen noise (BN) is electromagnetic pulse sequence that could be used to nondestructively predict the properties of materials such as hardness, residual stress and carbon content. Current BN signal analysis methods fail to describe the highly variated BN signal and achieve high regression accuracy due to the low interpretability of neural network and limited capacity of mathematical regression tools. In this paper, two multi-variable regression tools, named partial Chebyshev polynomial regression (PCPR) and Mutual Information-based Feature Selection with Class-dependent Redundancy and multi-variable Chebyshev polynomials regression (MIFS-CR+MCPR), are employed for the first time to predict the hardness of Cr12MoV steel (i.e. X12m). Combined with Chebyshev polynomials, ourregression tools are designed on the basis of cascaded regression and mutual-information-based feature selection. As represented by the experimental results for predicting the hardness of X12m, theproposed method outperforms other comparative methods including neural network and partial linear square regression method. © 2020 The authors and IOS Press.

Keyword:

Vanadium steel Nondestructive examination Electromagnetic pulse Forecasting Hardness Feature extraction Vanadium alloys Neural networks Molybdenum steel Ternary alloys Multivariable systems Polynomial regression Chromium alloys Polynomials Molybdenum alloys Chromium steel

Author Community:

  • [ 1 ] [Li, Zibo]Beijing JingHang Research Institute of Computation and Communication, Beijing; 100074, China
  • [ 2 ] [Li, Zibo]Classified Information Carrier Safety, Management Engineering Technology Research Center of Beijing, 100074, China
  • [ 3 ] [Li, Zibo]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Li, Shicheng]Beijing JingHang Research Institute of Computation and Communication, Beijing; 100074, China
  • [ 5 ] [Li, Shicheng]Classified Information Carrier Safety, Management Engineering Technology Research Center of Beijing, 100074, China
  • [ 6 ] [Wang, Donghao]Beijing JingHang Research Institute of Computation and Communication, Beijing; 100074, China
  • [ 7 ] [Wang, Donghao]Classified Information Carrier Safety, Management Engineering Technology Research Center of Beijing, 100074, China
  • [ 8 ] [Sun, Guangmin]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 9 ] [He, Cunfu]College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 10 ] [Li, Yu]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 11 ] [Liu, Xiucheng]College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 12 ] [Cai, Yanchao]College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 13 ] [Wang, Chu]Beijing JingHang Research Institute of Computation and Communication, Beijing; 100074, China
  • [ 14 ] [Wang, Chu]Classified Information Carrier Safety, Management Engineering Technology Research Center of Beijing, 100074, China

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Source :

ISSN: 1383-7281

Year: 2020

Volume: 45

Page: 179-185

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 1

30 Days PV: 4

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