Improving defect detection of nuclear welds using adaptive imaging based on uncertain material parameters optimization

Weld inspection in the nuclear sector is crucial to ensuring the structural integrity and safety of reactor cooling piping systems. Given strict safety requirements, reliable and accurate Non-Destructive Evaluation (NDE) techniques are essential for early defect detection and characterisation. In this context, phased-array ultrasonic imaging has emerged as a powerful tool for advanced NDE. Among these, the Total Focusing Method (TFM) with Full Matrix Capture (FMC) is widely recognised for its superior signal-to-noise ratio and imaging performance. Weld inspection is challenging due to the anisotropic and heterogeneous nature of weld microstructures, which distort wavefronts and increase structural noise, as well as the imprecise knowledge of these properties. This work addresses the latter issue, as TFM images become distorted when the Time of Flight (ToF) used in reconstruction deviates from the physical ToF. Correcting ToF is therefore necessary to compensate for these aberrations. This study enhances an existing adaptive imaging approach based on the TFM, the use of a complex weld model, and the optimisation of an imaging criterion within the space of the weld model's parameters. Unlike the initial version requiring prior knowledge of defect locations, the enhanced framework automatically identifies subzones with high defect likelihood. An improved global normalised criterion is then applied to enhance image quality. A more robust hybrid optimiser, combining global exploration and local refinement, is used, improving stability and computational cost. The approach was validated using experimental and simulated FMC datasets, showing improved defect detectability and image quality, and was implemented as a CIVA plugin.