Deep Learning and Machine learning Techniques in Advanced Non-Destructive Testing

Abstract

There has been considerable evidence of the effectiveness of machine learning (ML) and artificial intelligence (AI) algorithms as numerical tools; some of their applications include computational fluid dynamics, control and automation, signal processing, and materials engineering. Recent advances in ML and AI can directly benefit non-destructive testing, one of the most important industrial applications. With AI, data analysis can be improved and better harnessed. NDT uses electromagnetic waves or material-based methods to acquire information from a specimen. ML algorithms can interpret multiple signals or images to analyze, inspect, and examine the integrity of the materials structure. An overview of the foundations and current applications of ML techniques in advanced NDT is presented in this paper. In addition to explaining ML techniques, the most recent advances in ML and AI, including deep learning, are also discussed.