Characterization of subsurface cracks in eddy current testing using machine learning methods-Reference-Cited by-同舟云学术

Characterization of subsurface cracks in eddy current testing using machine learning methods

Published:2021-03-10 Issue: Volume: Page:
ISSN:0894-3370
Container-title:International Journal of Numerical Modelling: Electronic Networks, Devices and Fields
language:en
Short-container-title:Int J Numer Model

Author:

Barrarat Fatima¹^ORCID,Rayane Karim²,Helifa Bachir¹,Lefkaier Ibn Khaldoun¹

Affiliation:

1. Materials Physics Laboratory, Department of Material Sciences University of Laghouat Laghouat Algeria

2. Process Engineering Laboratory, Department of Mechanical Engineering University of Laghouat Laghouat Algeria

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Computer Science Applications,Modelling and Simulation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jnm.2876

Reference23 articles.

1. Improving Non-Destructive Test Results Using Artificial Neural NetworksImproving Non-Destructive Test Results Using Artificial Neural Networks

2. Defect Characterization With Eddy Current Testing Using Nonlinear-Regression Feature Extraction and Artificial Neural Networks

3. PeiXM LiangHS QiaYM.A frequency spectrum analysis method for eddy current nondestructive testing. Paper presented at: Proceedings of International Conference on Machine Learning and Cybernetics; November 4‐5 2002; Beijing China

4. Subsurface Defects Evaluation using Eddy Current Testing

5. The Pulsed Eddy Current Differential Probe to Detect a Thickness Variation in an Insulated Stainless Steel

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