AI for NDE 4.0 – Recent use cases
- NDE 4.0
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Copyright (c) 2022 Journal of Non-Destructive Testing and Evaluation (JNDE)
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The use of machine learning in non-destructive evaluation (NDE) is a growing trend in the industry and a necessary development towards NDE 4.0. Beside academia, there are numerous case examples where machine learning is in use for actual inspections already. The main benefit of machine learning powered NDE is its reliability and repeatability to find flaws from the data. However, as the fundamental is in image recognition, machine learning can facilitate the inspection in general beyond just finding defects. These can be recognising the welds, image quality indicators and other features which the inspectors usually have to identify by themselves. In addition to reliability increase, automating these repeatable tasks increase the speed of data analysis considerably, saving inspector’s time where it is most valuable. In this paper we review use cases where machine learning has been used in NDE and how these approaches benefit the end customer.
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