Defect Detection in CK45 Steel Structures through C-scan Images Using Deep Learning Method

作     者：Navid Moshtaghi Yazdani 

作者机构：Department of Electrical EngineeringMashhad branchIslamic Azad UniversityMashhadIran 

出 版 物：《Artificial Intelligence Advances》 (人工智能进展（英文）)

年 卷 期：2021年第3卷第1期

页      面：44-51页

学科分类：08[工学] 080502[工学-材料学] 0805[工学-材料科学与工程（可授工学、理学学位）] 

主　　题：Deep neural network Deep learning Magnetic field measurement Anisotropic magneto-resistive Machine learning CK45 steel 

摘      要：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 *** 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 *** 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 *** results confirm the superiority of the proposed method for characterizing defects compared to other classical training-oriented methods。