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Prediction of Inhomogeneous Stress in Metal Structures: A Hybrid Approach Combining Eddy Current Technique and Finite Element Method

Lookup NU author(s): Dr Cristian Ulianov, Professor Gui Yun Tian

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2021 Yating Yu et al.Concentrated stresses and residual ones are critical for the metal structures' health, because they can cause microcracks that require emergency maintenance or can result in potential accidents. Therefore, an accurate approach to the measurement of stresses is key for ensuring the health of metal structures. The eddy current technique is an effective approach to detect the stress according to the piezoresistive effect. However, it is limited to detect the surface stress due to the skin effect. In engineering, the stress distribution is inhomogeneous; therefore, to predict the inhomogeneous stress distribution, this paper proposes a nondestructive approach which combines the eddy current technique and finite element (FE) method. The experimental data achieved through the eddy current technique determines the relationship between the applied force and the magnetic flux density, while numerical simulations through the FE method bridge the relationship between the magnetic flux density and the stress distribution in different directions. Therefore, we can predict the inhomogeneous stress nondestructively. As a case study, the applied stress in a three-point-bending simply supported beam was evaluated, and the relative error is less than 8% in the whole beam. This approach can be expected to predict the residual stress in metal structures, such as rail and vehicle structures, if the stress distribution pattern is known.


Publication metadata

Author(s): Yu Y, Yuan F, Li H, Ulianov C, Tian G

Publication type: Article

Publication status: Published

Journal: Journal of Sensors

Year: 2021

Volume: 2021

Online publication date: 07/07/2021

Acceptance date: 15/06/2021

Date deposited: 12/08/2021

ISSN (print): 1687-725X

ISSN (electronic): 1687-7268

Publisher: Hindawi Limited

URL: https://doi.org/10.1155/2021/6647093

DOI: 10.1155/2021/6647093

Notes: In Speocal Issue: Sensors, Signal, and Artificial Intelligent Processing


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