Open AccessDefect Detection in CK45 Steel Structures through C-scan Images Using Deep Learning Method
Author(s) -
Navid Moshtaghi Yazdani
Publication year - 2021
Publication title -
artificial intelligence advances
Language(s) - English
Resource type - Journals
ISSN - 2661-3220
DOI - 10.30564/aia.v3i1.3074
Subject(s) - deep learning , artificial intelligence , artificial neural network , eddy current testing , computer science , perceptron , pattern recognition (psychology) , convolution (computer science) , convolutional neural network , eddy current , resistive touchscreen , materials science , computer vision , engineering , electrical engineering
In the present paper, a method for reliable estimation of defect profile in CK45 steel structures is presented using an eddy current testing based measurement system and post-processing system based on deep learning technique. So a deep learning method is used to determine the defect characteristics in metallic structures by magnetic field C-scan images obtained by an anisotropic magneto-resistive sensor. Having designed and adjusting the deep convolution neural network and applied it to C-scan images obtained from the measurement system, the performance of deep learning method proposed is compared with conventional artificial neural network methods such as multilayer perceptron and radial basis function on a number of metallic specimens with different defects. The results confirm the superiority of the proposed method for characterizing defects compared to other classical training-oriented methods.