Application of back-propagation neural networks to defect characterization using eddy current testing

Xinwu Zhou, Ryoichi Urayama, Tetsuya Uchimoto, Toshiyuki Takagi

Research output: Contribution to journalArticlepeer-review

Abstract

Eddy current testing is widely used for the automatic detection of defects in conductive materials. However, this method is strongly affected by probe scanning conditions and requires signal analysis to be carried out by experienced inspectors. In this study, back-propagation neural networks were used to predict the depth and length of unknown slits by analyzing eddy current signals in the presence of noise caused by probe lift-off and tilting. The constructed neural networks were shown to predict the depth and length of defects with relative errors of 4.6% and 6.2%, respectively.

Original languageEnglish
Pages (from-to)817-825
Number of pages9
JournalInternational Journal of Applied Electromagnetics and Mechanics
Volume64
Issue number1-4
DOIs
Publication statusPublished - 2020

Keywords

  • Eddy current testing
  • artificial intelligence
  • back-propagation neural network

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering
  • Electrical and Electronic Engineering

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