Application of Artificial Neural Network in Non-destructive Testing

Dear Colleagues,

Artificial neural networks (ANNs) have a significant role in solving non-destructive testing (NDT) problems. NDT systems, as nonlinear and complex systems, need this efficient tool for obtaining accuracy and better precision. The combination of ANNs with other methods, such as metaheuristic algorithms, feature extraction in time, frequency and time–frequency domains, feature reduction, feature selection, correlation analysis, etc., can improve the efficiency of NDT for obtaining better results for desired purposes.

This Special Issue of Electronics will provide a forum for discussing exciting research on applying AI (especially ANNs) in complex problems of NDT, such as thermography, tomography, multiphase flow metering (MFPM), radiographic testing (RT), electromagnetic testing (ET), visual testing (VT), ultrasonic testing (UT), acoustic emission (AE), shearography testing, etc. Both original research articles and comprehensive review papers are welcome.

Potential topics include, but are not limited to, the following:

• Artificial intelligence in NDT.
• ANNs in multiphase flow metering.
• Advances in the use of signal processing in NDT.
• Two- and three-phase flows.
• ANNs in tomography.
• Complex and nonlinear systems.
• Increasing the efficiency of NDT.
• Modeling of flows.
• The use of soft computing in NDT.
• Feature extraction, reduction, and selection.

Dr. Gholam Hossein Roshani
Guest Editor

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