Journal of Nondestructive Evaluation最新文献

This paper introduces a deep learning (DL)-enhanced X-ray computed tomography (CT) approach for detection of defects in composite structures. While X-ray CT offers high-fidelity defect detection, test specimen size limitations restrict its application to large aerospace components. Inclined CT (ICT) addresses these size constraints by keeping X-ray source and detector on the different sides of a stationary test specimen. This system geometry results in a limited angular data 3D reconstructions that produce significant artifacts that may represent defects incorrectly. This research demonstrates that DL techniques, particularly the fine-tuned Segment Anything Model (SAM), can improve defect detection from ICT data. Methodology employs fine-tuning of SAM with a dataset of 1,800 images across ten synthetic phantoms with varying defect sizes and locations. The fine-tuned model was validated on an as-built aluminum test specimen, achieving over 70% accuracy in defect detection and 98% accuracy in overall shape detection. Validation with carbon fiber reinforced polymer specimens containing Teflon inserts yielded improved results compared to ICT reconstruction methods, indicating practical applicability. The findings suggest that DL-enhanced ICT can offer detection capabilities comparable to full CT while preserving the large-structure compatibility of ICT, making it a viable non-destructive inspection method for aerospace industry applications.

Preserving aging bridges, which are vital to transportation networks, presents notable difficulties due to factors like intense usage, structural wear, and restricted maintenance resources. This research examines the deployment of Nondestructive Evaluation (NDE) techniques to optimize bridge maintenance strategies and maintain structural soundness. Over the course of infrastructure lifespans, vast amounts of NDE data are accumulated, yet processing and interpreting this information proves challenging due to intricate spatial and temporal interdependencies. In this study, we approach the problem as one of graph-based prediction, introducing two advanced methodologies to address it. The primary approach utilizes a Temporal Graph Convolution Network (TGCN), harnessing spatio-temporal patterns for predictive modeling. The secondary approach, a multi-modal TGCN, integrates data fusion techniques to combine diverse data sources for improved predictive accuracy. We evaluate the performance of these approaches using NDE data collected at Rutgers’ BEAST^® facility that includes five NDE modalities and 14 consecutive time intervals for assessing bridge deck conditions, comparing the results against a baseline Spatio-Temporal Autoregressive (STAR) model. While the STAR model established foundational forecasts, the TGCN method achieved superior results by managing nonlinearities. The multi-modal TGCN further enhanced performance, demonstrating the advantages of leveraging data fusion to incorporate multiple data types within TGCN frameworks.

Solid phase processing, such as friction stir processing, is an advanced manufacturing method that often results in ultrafine grain sizes and superior mechanical properties. The motivation of this study was to demonstrate ultrasonic testing as a nondestructive evaluation method to complement traditional destructive methods for characterizing material microstructure, with an emphasis on grain size determination using a method that may have future applications for real-time inline process monitoring and product validation. The method for measuring grain sizes of polycrystalline metals after solid phase processing was established using ultrasonic shear wave backscattering, building on prior studies on coarse-grained materials. The work involved measuring ultrasonic backscattering for a series of 316L stainless steel specimens with various grain sizes made by friction stir processing, calculating ultrasonic backscattering coefficients from experimental data based on a physical measurement model, measuring ground truth grain sizes of the specimens from electron backscatter diffraction grain boundary images, and building a correlation of ultrasonic backscattering coefficients versus the ground truth grain sizes. The grain sizes of a set of blind test specimens were successfully determined based on the correlation. This work successfully demonstrates the viability of an ultrasonic nondestructive evaluation method for microstructural characterization of material having ultrafine grain structure, as produced by an advanced manufacturing method.

In this study, the feasibility of a machine learning model for the automatic classification of impact-echo testing results was investigated. A machine learning model with features such as instantaneous frequency and spectral entropy extracted from time series data was compared with two different approaches, including conventional peak frequency and a deep learning model. To construct a robust and flexible model, an open-source database from two organizations performed by different testing operators and equipment was used to train and develop the universal classifier. The model was evaluated for its ability to classify the type of defects as well as their presence, and the results showed that shallow delamination can be detected more accurately than other types of defects. The proposed machine learning model showed reliable and promising results and has the potential to improve the efficiency of impact-echo testing in concrete structures.

Simulations of XCT systems, as employed in the context of the manufacturing and design process, represent a time-saving, cost- and resource-efficient alternative to repeated experimental measurements. This article is dedicated to the development and evaluation of various metrics that should enable an adequate verification and optimization of a XCT simulation of an experimental XCT system. The present study employed statistical evaluation as a methodological approach. The present article makes a significant contribution to the optimization of the development process of a XCT simulation and provides a foundation for future research activities in this field.

In lab experiments, it has been observed that the stress–and time-dependent elastic properties of a complex material at a structural scale perform accordingly to its composition at a microstructural level. We seek complementary practices to the current wavefield-based non-destructive testing techniques to assess not only the integrity level of civil structures but also the microstructural elements that contribute to it. In this paper, we study the systematic evolution of elastic properties of concrete as an alternative to investigate the density of micro imperfections in an outdoor-conditioned concrete structure. We estimate 5-second relative velocity changes in four locations on a Test bridge subjected to the action of vertical impulsive sources, at different prestressing levels (dynamic effects at different static conditions). We describe the structure’s stress- and time-dependent elastic response by means of acoustoelastic effect and Slow-dynamic processes, respectively. We also estimate the conventional ultrasound pulse velocity and perform a cooperative integrity analysis of the structure using the three elastic phenomena. Our findings reveal: 1) The presence of soft microstructures and their orientation’s influence on the acoustoelastic effect and Slow-dynamics in field-conditioned concrete structures. 2) The relation of low ultrasound pulse velocities with high acoustoelastic effect and high magnitudes and variability of Slow-dynamics. 3) Different elastic behaviours on the north and south spans of the bridge, suggesting different heterogeneity levels on the analysed locations of the concrete beam.

Inductive thermography is a non-destructive testing (NDT) method used for checking friction stir welding (FSW) joints, which can have defects like tunneling. In this research, inductive thermography was used to find tunneling defects in three FSW samples that had already been looked at with radiography and ultrasonic testing. Using thermal signal reconstruction (TSR) techniques in MATLAB made the thermography images clearer, helping to identify defects that were hard to see otherwise. To make defect detection more accurate, an image fusion method was used. This combined thermography and radiographic images and then checked them against ultrasonic images to confirm the findings. The fusion process in MATLAB helped combine different types of data to give a fuller view of the defects, thus improving the identification of defects like tunneling in FSW joints. The study shows that inductive thermography when paired with image fusion, provides quicker, safer, and cheaper defect detection compared to classical methods like radiography. Merging multiple NDT methods through data fusion improves accuracy in finding defects, leading to better reliability and safety in welded structures.

This paper presents the development of a monitoring system using acoustic emission (AE) analysis for the prediction of micro- and initial cracks in fatigue-stressed steel structures such as bridges, cranes, offshore, or industrial constructions. Initial experimentation suggests a relationship between microscopically observed crack length and AE intensity, further data is required to establish a definitive correlation. As part of an ongoing research project, AE measurement techniques and evaluation are to be further developed to create a monitoring concept for micro-crack prediction in more complex fatigue-stressed steel components. The focus of this research is not on the localization and detection of crack growth or structural changes but on micro-crack detection using AE. Existing acoustic emission analysis systems can thus be extended to measure and detect micro-cracks for the earliest possible identification of damage events. This paper describes the first results of the innovative research idea.

To maximize power generation and enhance energy conversion efficiency, wind turbine blades have been increasingly scaled up. As the primary component responsible for capturing wind energy, these blades are particularly vulnerable to damage under harsh environmental conditions. Additionally, due to the remote locations, expansive areas, and unmanned operations of wind farms, regular inspections are crucial to maintaining safe operation. This paper presents a lightweight small object detection algorithm (LSOD-YOLO) based on YOLOv8, designed for detecting surface damage on wind turbine blades using drone aerial imagery. To tackle the challenge of detecting small objects on wind turbine surfaces, LSOD-YOLO incorporates Omni-dimensional Dynamic Convolution (ODConv) into the C2f module. The neck network is subsequently improved with the Scale Sequence Feature Fusion (SSFF) module and the Triple Feature Encoder (TFE) module. Furthermore, a small object detection layer is introduced to capture additional shallow feature information. These refinements enhance the algorithm’s capacity to detect small objects while preserving accuracy for other target sizes. To achieve a lightweight model design, a strategy involving parameter sharing and partial convolution is employed to optimize the detection head structure. This approach significantly reduces computational load while preserving accuracy. Experimental results on the wind turbine surface damage dataset demonstrate that the proposed LSOD-YOLO algorithm surpasses the baseline in both detection accuracy and model size, facilitating low-latency real-time inference with a notable performance enhancement.

In this study, computer vision and deep learning was combined to develop a rapid method for quantifying the astaxanthin isomer content in krill meal. A total of 310 Antarctic krill meal samples were collected and their astaxanthin isomer content was determined as observed values using high-performance liquid chromatography. A computer vision system was then used to acquire images of the krill meal samples, which were subsequently preprocessed and fed into a Convolutional Neural Network (CNN) to establish a predictive model; its performance was compared with that of a feature-based artificial neural networks model. The results showed that the 13-cistrine (13-Cis) astaxanthin, all-trans astaxanthin, and 9-cistrine (9-Cis) astaxanthin content were distributed in the range of 0–2.05 mg/kg, 0.09–62.97 mg/kg, and 0–7.58 mg/kg, respectively. For the test set, CNN achieved an R² of 0.96 in predicting all-trans astaxanthin and an R² of 0.88 for 9-Cis astaxanthin. In out-of-sample validation, the CNN achieved mean relative errors of 5.20% and 11.35% for all-trans and 9-Cis astaxanthin, respectively. In conclusion, computer vision combined with CNN offers an efficient, precise, and non-destructive technique for quantitatively analysing astaxanthin isomers in krill meal.