Post-Processing of Phased-Array Ultrasonic Inspection Data with Parallel Computing for Nondestructive Evaluation

Phased-array ultrasonic nondestructive evaluation is an effective tool of safety assurance for key structural components. The paper presents a general post-processing methodology for phased-array ultrasonic inspection data. The methodology is developed to integrate three components: mapping of sampling points to structure model, re-sampling from non-uniformly distributed sampling points in phased-array to a uniform volume, and data fusion strategies for multiple channels. An adaptive method called spatially adaptive Gaussian splatting is proposed for data re-sampling and fusion considering the reconstruction resolution and local characteristics of ultrasonic sound paths. This adaptivity provides a viable approach to minimize the effects of under-sampling, over-sampling, and holes which are introduced by the non-uniformly distributed sampling points. The processing of large scale data through segmentation and parallelization techniques is discussed in detail. The effectiveness and performance of the proposed methodology are investigated using actual phased-array ultrasonic testing data.