Plenary Paper: Next-generation NDT: how artificial intelligence will define the future of inspection and structural health monitoring
R Gr Maev
University of Windsor, Canada
Non-destructive testing (NDT) and structural health monitoring (SHM) are crucial processes utilised in industries such as aerospace, automotive, oil & gas transportation, structural engineering and manufacturing, etc, to ensure the integrity and reliability of materials and structures, without causing any damage. Artificial intelligence (AI) has the potential to revolutionise NDT and SHM technologies in the future due to the integration of AI into NDT/SHM applications, which promises to greatly enhance the efficiency, accuracy and speed of inspections.
One of the significant areas where AI can have a profound impact on NDT is data analysis. With the exponential growth of data, AI-powered algorithms can quickly and accurately analyse vast amounts of information. This capability significantly reduces time-consuming manual efforts and enhances the ability to identify patterns and anomalies. For instance, in the aviation field, AI-based NDT/SHM various sensors can analyse data from sensors embedded in aircraft components during flight. By assessing the collected data, AI algorithms can detect potential defects or irregularities, allowing for proactive maintenance and minimising the risk of catastrophic failures.
One of the significant areas where AI can have a profound impact on NDT is data analysis. With the exponential growth of data, AI-powered algorithms can quickly and accurately analyse vast amounts of information. This capability significantly reduces time-consuming manual efforts and enhances the ability to identify patterns and anomalies. For instance, in the aviation field, AI-based NDT/SHM various sensors can analyse data from sensors embedded in aircraft components during flight. By assessing the collected data, AI algorithms can detect potential defects or irregularities, allowing for proactive maintenance and minimising the risk of catastrophic failures.
