Authors: Guang An Ooi, Mehmet Ozakin, Tarek Mostafa, and Moutazbellah Khater (King Abdullah University of Science and Technology (KAUST)); Ahmed Aljarro (Saudi Aramco); and Hakan Bagci and Shehab Ahmed (King Abdullah University of Science and Technology (KAUST))
Abstract: A new machine learning-based non-destructive testing (NDT) technique for the examination of conductive objects is presented. NDT of objects behind barriers utilize the defect-induced distortions on electromagnetic (EM) fields to detect flaws in the structure of inspected targets. Such distortions are highly non-linear, requiring significant amounts of data for training neural networks. To this end, a massively parallelized data generation framework is proposed in conjunction with a multi-frequency hybrid neural network (MF-HNN), to create a physics-informed inversion AI model. The performance of the resulting inversion algorithm is applied on casings, where tubular pipes are inspected. For data generation, physics-based solvers are employed to simulate the EM field distribution resulting from pipes with defects. The large-scale distribution of this step leads to 43 times faster execution than a single CPU. This allows the MF-HNN to achieve significantly improved generalization performance and to generate high-resolution cross-sectional images of the pipelines.
Best Poster Finalist (BP): no
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