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Deep neural network for simulation of magnetic flux leakage testing

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Bibliographic Details
Main Author: Minhhuy Le
Other Authors: Cong-Thuong Pham
Format: Article
Language:English
Published: Measurement 2021
Subjects:
Deep learning
Machine learning
MFLT
FEM
Online Access:https://www.sciencedirect.com/science/article/abs/pii/S0263224120312306?via%3Dihub#!
https://dlib.phenikaa-uni.edu.vn/handle/PNK/1933
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spelling oai:localhost:PNK-19332022-08-17T05:54:45Z Deep neural network for simulation of magnetic flux leakage testing Minhhuy Le Cong-Thuong Pham Jinyi Lee Deep learning Machine learning MFLT FEM Q1 Magnetic flux leakage testing (MFLT) is an important nondestructive testing method for the detection and evaluation of defects in magnetic materials. Magnetic field distribution in an MFLT system is usually simulated by the finite element method (FEM), which required large memory, high computation, and complication of the meshing process. In this paper, an alternative simulation method will be proposed using a deep neural network (DNN). The DNN method provides an easy way of simulation by feeding only the distribution of supplied current and the physical properties such as magnetic permeability without the need for the meshing process. Defects with arbitrary sizes were simulated under different configurations of the MFLT systems. The DNN was trained on the simulation results of the FEM and provided an accurate prediction of the magnetic field distribution of the unseen data. This study paves the way for designing optimized MFLT systems in a bigdata-driven method. 2021-07-05T08:00:04Z 2021-07-05T08:00:04Z 2021 Article https://www.sciencedirect.com/science/article/abs/pii/S0263224120312306?via%3Dihub#! https://dlib.phenikaa-uni.edu.vn/handle/PNK/1933 10.1016/j.measurement.2020.108726 en application/pdf Measurement
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic Deep learning
Machine learning
MFLT
FEM
spellingShingle Deep learning
Machine learning
MFLT
FEM
Minhhuy Le
Deep neural network for simulation of magnetic flux leakage testing
description Q1
author2 Cong-Thuong Pham
author_facet Cong-Thuong Pham
Minhhuy Le
format Article
author Minhhuy Le
author_sort Minhhuy Le
title Deep neural network for simulation of magnetic flux leakage testing
title_short Deep neural network for simulation of magnetic flux leakage testing
title_full Deep neural network for simulation of magnetic flux leakage testing
title_fullStr Deep neural network for simulation of magnetic flux leakage testing
title_full_unstemmed Deep neural network for simulation of magnetic flux leakage testing
title_sort deep neural network for simulation of magnetic flux leakage testing
publisher Measurement
publishDate 2021
url https://www.sciencedirect.com/science/article/abs/pii/S0263224120312306?via%3Dihub#!
https://dlib.phenikaa-uni.edu.vn/handle/PNK/1933
_version_ 1751856255754829824
score 8.881002

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