Validated and Deployable AI/ML for NDT Data Diagnostics

Abstract

While artificial intelligence/machine learning (AI/ML) methods have shown promise for the analysis of image and signal data, applications using nondestructive testing (NDT) for managing the safety of systems must meet a high level of quantified capability. Engineering decisions require technique validation with statistical bounds on performance to enable integration into critical analyses, such as life management and risk analysis. The Air Force Research Laboratory (AFRL) has pursued several projects to apply a hybrid approach that integrates AI/ML methods with heuristic and model-based algorithms to assist inspectors in accomplishing complex NDT evaluations. Three such examples are described in this article, including a method that was validated through a probability of detection (POD) study and deployed by the Department of the Air Force (DAF) in 2004 (Lindgren et al. 2005). Key lessons learned include the importance of considering the wide variability present in NDT applications upfront and maintaining a critical role for human inspectors to ensure NDT data quality and address outlier indications.