Ultrasonic Defect Characterization Using the Scattering Matrix: A Performance Comparison Study of Bayesian Inversion and Machine Learning Schemas

Ultrasonic Rough Crack Characterization Using Time-of-Flight Diffraction With Self-Attention Neural Network

Time-of-flight diffraction (ToFD) is a widely used ultrasonic nondestructive evaluation (NDE) method for locating and characterizing rough defects, with high accuracy in sizing smooth cracks. However, naturally grown defects often have irregular surfaces, complicating the received tip diffraction waves and affecting the accuracy of defect characterization. This article proposes a self-attention (SA) deep learning method to interpret the ToFD A-scan signals for sizing rough defects. A high-fidelity finite-element (FE) simulation software Pogo is used to generate the synthetic datasets for training and testing the deep learning model. Besides, the transfer learning (TL) method is used to fine-tune the deep learning model trained by the Gaussian rough defects to boost the performance of characterizing realistic thermal fatigue rough defects. An ultrasonic experiment using 2-D rough crack samples made by additive manufacturing is conducted to validate the performance of the developed deep learning model. To demonstrate the accuracy of the proposed method, the crack characterization results are compared with those obtained using the conventional Hilbert peak-to-peak sizing method. The results indicate that the deep learning method achieves significantly reduced uncertainty and error in rough defect characterization, in comparison with traditional sizing approaches used in ToFD measurements.

Systematic Evaluation of Ultrasonic In-Line Inspection Techniques for Oil and Gas Pipeline Defects Based on Bibliometric Analysis

The global reliance on oil and gas pipelines for energy transportation is increasing. As the pioneering review in the field of ultrasonic defect detection for oil and gas pipelines based on bibliometric methods, this study employs visual analysis to identify the most influential countries, academic institutions, and journals in this domain. Through cluster analysis, it determines the primary trends, research hotspots, and future directions in this critical field. Starting from the current global industrial ultrasonic in-line inspection (ILI) detection level, this paper provides a flowchart for selecting detection methods and a table for defect comparison, detailing the comparative performance limits of different detection devices. It offers a comprehensive perspective on the latest ultrasonic pipeline detection technology from laboratory experiments to industrial practice.

Characterization of three-dimensional surface-breaking slots based on regression analysis of ultrasonic Rayleigh wave simulations

Rayleigh waves travel along the surface of a solid structure, with most of their energy focusing within a depth of one wavelength. Thus, the reflection coefficient from a surface-breaking crack is highly sensitive to the ratio between the crack depth and the wavelength. It is possible to characterize the depth of surface-breaking cracks by measuring the features in the reflected waves. However, a feature value can correspond to multiple depth-wavelength ratios, i.e., the mapping is non-univalent, which brings difficulties for crack sizing using the feature. In this work, we use finite element method (FEM) software to perform 3-D numerical analysis on the interaction between Rayleigh waves and surface-breaking slots with various 3-D geometries. Multiple features are selected based on the nearest neighbour regression analysis on a numerical dataset, ensuring that a univalent mapping relationship from the selected features to the slot depth can be established. This relationship is then experimentally used to predict the depth of real slots with different geometries, showing reasonable accuracy.

Ultrasonic characterization of small defects based on Res-ViT and unsupervised domain adaptation

This paper investigated the application of deep neural networks and domain adaptation for ultrasonic characterization of elliptical defects that are small and inclined. Based on performance evaluation of deep residual network (ResNet) and vision transformer (ViT), we proposed a novel Res-ViT architecture which fuses deep representative features of both models. Furthermore, we developed an unsupervised domain adaptation method to minimize the distance between the source and target domains, which is measured by maximum mean discrepancy. This approach serves to improve the generalizability of the proposed Res-ViT model in noisy environments. Simulation studies were performed at various noise levels to evaluate robustness of different deep neural networks. The proposed Res-ViT model was shown to reduce the characterization uncertainty of various defect parameters, including size, angle, and aspect ratio. Experiments were performed on three elliptical defects which have large orientation angles of 60∘ relative to the array direction. The proposed method achieved a 61% reduction in the root-mean-square error (RMSE) of defect size compared to a benchmark approach, which is based on principal component analysis and the nearest neighbor method.

Long-Term Ultrasonic Benchmarking for Microstructure Characterization with Bayesian Updating

Ultrasonic non-destructive characterization is an appealing technique for identifying the microstructures of materials in place of destructive testing. However, the existing ultrasonic characterization techniques do not have sufficient long-term gage repeatability and reproducibility (GR&R), since benchmarking data are not updated. In this study, a hierarchical Bayesian regression model was utilized to provide a long-term ultrasonic benchmarking method for microstructure characterization, suitable for analyzing the impacts of experimental setups, human factors, and environmental factors on microstructure characterization. The priori distributions of regression parameters and hyperparameters of the hierarchical model were assumed and the Hamilton Monte Carlo (HMC) algorithm was used to calculate the posterior distributions. Characterizing the nodularity of cast iron was used as an example, and the benchmarking experiments were conducted over a 13-week transition period. The results show that updating a hierarchical model can increase its performance and robustness. The outcome of this study is expected to pave the way for the industrial uptake of ultrasonic microstructure characterization techniques by organizing a gradual transition from destructive sampling inspection to non-destructive one-hundred-percent inspection.

Improved Unsupervised Learning Method for Material-Properties Identification Based on Mode Separation of Ultrasonic Guided Waves

Numerical methods, including machine-learning methods, are now actively used in applications related to elastic guided wave propagation phenomena. The method proposed in this study for material-properties characterization is based on an algorithm of the clustering of multivariate data series obtained as a result of the application of the matrix pencil method to the experimental data. In the developed technique, multi-objective optimization is employed to improve the accuracy of the identification of particular parameters. At the first stage, the computationally efficient method based on the calculation of the Fourier transform of Green’s matrix is employed iteratively and the obtained solution is used for filter construction with decreasing bandwidths providing nearly noise-free classified data (with mode separation). The filter provides data separation between all guided waves in a natural way, which is needed at the second stage, where a more laborious method based on the minimization of the slowness residuals is applied to the data. The method might be further employed for material properties identification in plates with thin coatings/interlayers, multi-layered anisotropic laminates, etc.

Towards Explainable Augmented Intelligence (AI) for Crack Characterization

Crack characterization is one of the central tasks of NDT&E (the Non-destructive Testing and Evaluation) of industrial components and structures. These days data necessary for carrying out this task are often collected using ultrasonic phased arrays. Many ultrasonic phased array inspections are automated but interpretation of the data they produce is not. This paper offers an approach to designing an explainable AI (Augmented Intelligence) to meet this challenge. It describes a C code called AutoNDE, which comprises a signal-processing module based on a modified total focusing method that creates a sequence of two-dimensional images of an evaluated specimen; an image-processing module, which filters and enhances these images; and an explainable AI module—a decision tree, which selects images of possible cracks, groups those of them that appear to represent the same crack and produces for each group a possible inspection report for perusal by a human inspector. AutoNDE has been trained on 16 datasets collected in a laboratory by imaging steel specimens with large smooth planar notches, both embedded and surface-breaking. It has been tested on two other similar datasets. The paper presents results of this training and testing and describes in detail an approach to dealing with the main source of error in ultrasonic data—undulations in the specimens’ surfaces.