Vol. 21 No. 4 (2024): Journal of Non Destructive Testing and Evaluation (JNDE), Dec 2024
Research Papers

Artificial Intelligence in NDE – Discussing the Infrastructure and Application of Critical Image Detection (CiD) and Single Image Detail Analysis (SiDA) in Manufacturing use Cases

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  • Maximilian Topp,
  • Christian Els,
  • Laurenz Strothmann,
  • Frederik Strothmann,
  • Arkadius Gombos

Published 11-12-2024

Keywords

  • Artificial Intelligence,
  • Critical Image Detection,
  • Single Image Detail Analysis,
  • NDE 4.0

How to Cite

Maximilian Topp, Christian Els, Laurenz Strothmann, Frederik Strothmann, & Arkadius Gombos. (2024). Artificial Intelligence in NDE – Discussing the Infrastructure and Application of Critical Image Detection (CiD) and Single Image Detail Analysis (SiDA) in Manufacturing use Cases. Journal of Non-Destructive Testing & Evaluation (JNDE), 21(4), 42–48. Retrieved from https://jnde.isnt.in/index.php/JNDE/article/view/97

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Abstract

                                                                                

The world of Artificial Intelligence (AI) in non-destructive testing (NDT) is changing fast, while mainly Automated Defect Recognition (ADR) is in focus of the discussion, the authors recently introduced a new approach to AI in NDT and NDE called “Critical Image Detection” (CiD) and “Single Image Detail Analysis” (SiDA), which prioritize finding critical images / parts first and can apply several algorithms to improve the evaluation of the respective data at hand. This paper describes the requirements for a digital NDE infrastructure to use these latest technologies. It also discusses the application of the new approaches (CiD/SiDA) in manufacturing and how they may enable companies to optimize their NDT/NDE and adjacent processes. It includes various applications of AI and other technologies from the NDT/NDE 4.0 technology stack such as image enhancement / super-resolution (CT use case), robotics (VT use case), anomaly detection (VT use case) and defect detection / recognition (VT, DR use cases). The paper draws on real-world case studies conducted by the authors, highlighting practical applications. It also provides an outlook on further analytics e.g. for predictive maintenance.

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