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Crack characterization in ferromagnetic steels by pulsed eddy current technique based on GA-BP neural network model

Lookup NU author(s): Professor Gui Yun Tian, Dr Wai Lok Woo

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).


Abstract

© 2020 Elsevier B.V.Ferromagnetic steels are widely used in engineering structures such as rail track, oil/gas pipeline and steel hanging bridge. Cracks resulted from manufacturing processes or previous loading will seriously undermine the safety of the engineering structures and even lead to catastrophic industrial accidents. Accurate and quantitative characterization the cracks in ferromagnetic steels are therefore of vital importance. In this paper, the cracks in ferromagnetic steels are detected by the pulsed eddy current (PEC) technique. Firstly, the physical mechanism of the relative magnetic permeability of the ferromagnetic steel on the detection signal of PEC is interpreted from a microscopic level of magnetic domain wall movement. The relationship of the crack width/depth and the detection signal of PEC is then investigated and verified by numerical simulations and experimental study. Finally, the cracks are inversely characterized by using Genetic Algorithm (GA) based Back-Propagation (BP) neural network (NN) considering the nonlinearity of the crack geometric parameters with the detection signal of PEC. The prediction results indicated that the proposed algorithm can characterize the crack depth and width within the relative error of 10%. The proposed approach combining PEC and GA based BPNN has been verified to quantitatively detect cracks in ferromagnetic steel.


Publication metadata

Author(s): Wang Z, fei Y, Ye P, Qiu F, Tian G, Woo WL

Publication type: Article

Publication status: Published

Journal: Journal of Magnetism and Magnetic Materials

Year: 2020

Volume: 500

Print publication date: 15/04/2020

Online publication date: 09/01/2020

Acceptance date: 06/01/2020

Date deposited: 30/03/2020

ISSN (print): 0304-8853

ISSN (electronic): 1873-4766

Publisher: Elsevier B.V.

URL: https://doi.org/10.1016/j.jmmm.2020.166412

DOI: 10.1016/j.jmmm.2020.166412


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