Nondestructive Evaluation Technologies Research Articles

Enhancing NDE Reliability for Grade 91 Steel Welds: Ultrasonic Imaging and Microstructural Correlations

Ensuring the integrity of Grade 91 (9Cr-1Mo-V) steel welds is vital for the safe and reliable operation of fossil fuel–fired and nuclear power plants. This study applies an imaging technique for the ultrasonic characterization of two Grade 91 steel welds created with cold metal transfer and flux-cored arc welding processes. Ultrasonic immersion testing in the through-transmission configuration was employed to generate shear waves, which helped identify the weld metal, heat-affected zone, and base metal regions. These weld microstructures were also correlated to their ultrasonic images using metallography, ultrasonic amplitude, hardness measurements, and grain size. The findings from this study can assist practitioners in developing new nondestructive evaluation technologies, improving the inspection reliability of creep strength–enhanced ferritic steel welds by potentially identifying weld microstructure regions susceptible to creep-type failures.

Railcar Axle Inspection Current State, Future Needs, and Opportunities

This tutorial article briefly overviews the current state-of-the-art railcar axle inspection methods to detect and characterize axle discontinuities or indications. This paper also presents and discusses some of the initiatives conducted by MxV Rail (formerly TTCI) and other researchers on the use of emerging and advanced nondestructive evaluation (NDE) technologies that are at different phases of development and not commercially available. Finally, a brief discussion is provided on the need for novel in-motion axle inspection NDE methods for moving trains. Such technology would enhance the safety of railcar axles. The need depends on the regulations in a given jurisdiction and on the class of service (passenger, freight, certain regulated hazardous materials).

Birefringence Technique for Evaluating Thermal Stresses in Railroad Rails

This paper discusses the development of an in situ noncontact electromagnetic acoustic transducer (EMAT) nondestructive evaluation technology to determine rail neutral temperature and estimate rail stress in continuous welded rail (CWR). Stresses develop in CWR due to a lack of expansion joints to accommodate thermal expansion and contraction of the rail when ambient temperatures vary over time. The novelty of the work presented is the usage of ultrasonic birefringence properties using EMATs to estimate thermally induced stresses in rails. EMATs produce polarized shear waves propagating through the rail web in the pulse-echo mode. Experimental tests were performed on machined 136RE and 141RE rail material with applied compressive and tensile stresses to explore the stress-birefringence behavior. Two additional sets of experimental tests were conducted on full-size rail sections with in situ surface conditions to study variations in the in situ birefringence and the acoustic stress constant in different rail materials including 115RE rail, 119RE rail, two different 136RE rails, and 141RE rail. The results show a highly linear relationship between the stresses applied and the measured acoustic birefringence.

A Dataset of Pulsed Thermography for Automated Defect Depth Estimation

Pulsed thermography is an established nondestructive evaluation technology that excels at detecting and characterizing subsurface defects within specimens. A critical challenge in this domain is the accurate estimation of defect depth. In this paper, a new publicly accessible pulsed infrared dataset for PVC specimens is introduced. It was enriched with 3D positional information to advance research in this area. To ensure the labeling quality, a comparative analysis of two distinct data labeling methods was conducted. The first method is based on human domain expertise, while the second method relies on 3D CAD images. The analysis showed that the CAD-based labeling method noticeably enhanced the precision of defect dimension quantification. Additionally, a sophisticated deep learning model was employed on the data, which were preprocessed by different methods to predict both the two-dimensional coordinates and the depth of the identified defects.

Integrating Data from Multiple Nondestructive Evaluation Technologies Using Machine Learning Algorithms for the Enhanced Assessment of a Concrete Bridge Deck

Several factors impact the durability of concrete bridge decks, including traffic loads, fatigue, temperature changes, environmental stress, and maintenance activities. Detecting problems such as corrosion, delamination, or concrete degradation early on can lower maintenance costs. Nondestructive evaluation (NDE) techniques can detect these issues at early stages. Each NDE method, meanwhile, has limitations that reduce the accuracy of the assessment. In this study, multiple NDE technologies were combined with machine learning algorithms to improve the interpretation of half-cell potential (HCP) and electrical resistivity (ER) measurements. A parametric study was performed to analyze the influence of five parameters on HCP and ER measurements, such as the degree of saturation, corrosion length, delamination depth, concrete cover, and moisture condition of delamination. The results were obtained through finite element simulations and used to build two machine learning algorithms, a classification algorithm and a regression algorithm, based on Random Forest methodology. The algorithms were tested using data collected from a bridge deck in the BEAST® facility. Both machine learning algorithms were effective in improving the interpretation of the ER and HCP measurements using data from multiple NDE technologies.

Multi-NDE Technology Approach to Improve Interpretation of Corrosion in Concrete Bridge Decks Based on Electrical Resistivity Measurements.

This research aimed to improve the interpretation of electrical resistivity (ER) results in concrete bridge decks by utilizing machine-learning algorithms developed using data from multiple nondestructive evaluation (NDE) techniques. To achieve this, a parametric study was first conducted using numerical simulations to investigate the effect of various parameters on ER measurements, such as the degree of saturation, corrosion length, delamination depth, concrete cover, and the moisture condition of delamination. A data set from this study was used to build a machine-learning algorithm based on the Random Forest methodology. Subsequently, this algorithm was applied to data collected from an actual bridge deck in the BEAST® facility, showcasing a significant advancement in ER measurement interpretation through the incorporation of information from other NDE technologies. Such strides are pivotal in advancing the reliability of assessments of structural elements for their durability and safety.

A reliability study on automated defect assessment in optical pulsed thermography

Nowadays, the reliability of data analysis and decision-making based on deep learning (DL) remains a primary concern in promoting DL technology for industrial non-destructive testing and evaluation (NDT&E). This study focuses on the quantitative assessment of the reliability of various automated data analysis techniques in NDT&E. To achieve this, optical pulsed thermography was employed to inspect three non-planar carbon-fiber-reinforced polymer (CFRP) samples, each containing embedded Teflon to simulate debonding defects. After applying thermographic signal reconstruction and first-order derivation processing to the raw thermal data, automated analysis was performed using three image-processing-based segmentation methods and three sequential-signal-based DL algorithms. Additionally, an experienced inspector manually analyzed the data for comparison purposes. Subsequently, the probability of detection and false positive analyses were conducted to quantitatively evaluate and compare the reliability of the automated and human-based evaluations. The comparison results demonstrated that optimal DL classification and advanced image-processing-based segmentation techniques could achieve performance levels close to that of human inspectors in defect detection, even for challenging non-planar CFRP samples.

Infrared imaging for detection of defects in concrete structures

Deterioration of concrete structures is an issue that affects the entire world and is brought on by various causes like climatic conditions, aging, attack of aggressive media, etc. Periodic inspections and proper maintenance are crucial to prevent the degradation of structures. Conventionally, the condition of the structures is monitored visually but over a while, non-destructive evaluation (NDE) technologies have gained significance and are expected to be successfully used for the inspection and management of civil structures. Infrared imaging is an effective NDT tool to monitor the health of civil structures. In this study, Infrared Thermography (IRT), an image-based method, is introduced and implemented for the assessment of concrete structures. Experiments on cement concrete cubes were conducted to investigate and enhance the applicability of the infrared thermography technique. The results are quite promising and helps to detect defects like delamination, voids, and cracks, etc. as the IR camera can pick-up the difference in temperature between sound and defective regions. However, the utilization of IRT for inspection poses many challenges but they can be overcome by combining it with other techniques like finite element method, acoustic emission and ultrasonic guided waves. Hence, the infra-red thermography technique is an excellent technology for practically evaluating concrete structures.

Miniaturized multi-modality field-ready sensing system for defect detection of CFRP materials

As infrastructure decays rapidly due to structural and material aging and failures, the development of new improved, and efficient nondestructive evaluation (NDE) techniques is vital for system health monitoring and preventive maintenance. Composite material, such as carbon fiber reinforced polymer (CFRP) plays a significant role in energy and transportation infrastructure due to the advantages of corrosion resistance, durability, and lightweight, which contributes to minimal maintenance and long service life. However, due to their unique anisotropic dielectric and mechanical characteristics, detecting composite material defects by NDE techniques is still challenging. This paper presents a novel, low-cost miniaturized multi-modality imaging system combining low-frequency capacitive shortwave and high-frequency microwave NDE technologies to detect various types of defects in CFRP materials. Based on the governing electromagnetic theory behind multi-modality electromagnetic NDE methods, the merit of combining low-frequency shortwave and near-field microwave imaging for dielectric characterization of composite materials is thoroughly investigated and demonstrated. Then, a miniaturized imaging system is developed to operate at ultra-wide bands: 10 kHz to 200 MHz for the capacitive shortwave probes and 1 GHz to 9 GHz for the microwave probes. Customized multi-material joining samples of CFRP and steel with different defect types, locations, depths, and sizes are tested by the developed imaging system, and the experimental results of the miniaturized system are compared with the existing table-top systems, which demonstrates comparatively accurate results for the developed multi-modality imaging system. The compact and practical nature of the presented imaging system makes it an optimal tool that can be utilized in field conditions with constrained operational spaces and NDE uncertainties.

Development of reference specimens for non-destructive evaluation of concrete bridge decks

Decades of non-destructive evaluation (NDE) for the quality assurance of concrete bridges revealed the necessity of developing procedures for building reference specimens for different defects. External objects have been widely used in specimen construction to represent defects. However, embedded materials can alter the actual defect characteristics, interrupting the NDE responses. This study proposes new approaches to fabricating reference specimens for reinforced concrete that feature substantial defects, including cracks, delamination, honeycombing and rebar corrosion, without external objects. As a result, realistic responses can be acquired to identify promising NDE methods for characterising actual deteriorations. The procedures were produced with the intent of being reproducible in any laboratory. Destructive and non-destructive testing methods were performed to verify the effectiveness of the processes in creating defects. This study provides research laboratories with techniques for fabricating reference specimens for characterising defects and assessing the performance of NDE technologies.

Nonlinear Ultrasonic Detection Enhanced by 3-D Printed Phononic Crystals

Nonlinear ultrasonic methods are a novel non-destructive testing and evaluation technology due to their high sensitivity to micro-defects. However, the inherent nonlinearity of ultrasonic equipment hinders the feature extraction with micro-defects information and limits the full-scale use of nonlinear ultrasonic NDT techniques. In this work, we propose a material-enabled filter using phononic crystals (PCs) as functional units to eliminate the unapplicable inherent nonlinearity component. The PCs composed of mass blocks and connective layers symmetrical about the host plate, are simultaneously processed by melting deposition of PLA. The finite element method with Bloch–Floquet boundary condition is implemented to obtain the bandgap at 100 kHz. Based on the mass-spring model, the mechanism of bandgap formation is proved to be attributed to the negative effective mass density band induced by the local resonance of mass block. The PCs successfully opens a propagation wave transmission gap and eliminates the inherent nonlinearity to improve the nonlinear detection of micro-defects in the aluminum plate. This work provides a useful methodology for the wide application of PC-based nonlinear detection methods in the non-destructive testing field.

Advancing Condition Assessment of Reinforced Concrete Bridge Elements Through Automation, Visualization, and Improved Interpretation of Multi-NDE Technology Data

Economic bridge management requires accurate information about the condition of bridges in the network. Nondestructive evaluation (NDE)has shown high potential in providing accurate condition assessment and, through periodic surveys, development of accurate deterioration, predictive, and life-cycle cost models. To achieve wide adoption by transportation agencies, further advances should be made that would lead to the accuracy of NDE-based condition assessment, reduced costs and traffic interruptions, and minimized risk to transportation workers. The paper discusses the following areas of improvement: increased speed and safety of data collection through the use of robotic systems, and improved data interpretation through visualization and joint analysis of data collected by multiple NDE technologies.

Detectability of Subsurface Defects in Polypropylene/Glass Fiber Composites Using Multiple Lock-In Frequency Modulated Algorithms

Recently, thermographic testing has been applied as a non-destructive evaluation technology for composite materials due to its remarkable advantages, such as its non-contact, non-destructive, and high-speed properties, it is harmless to the human body and easy to use, as well as having both qualitative and quantitative inspection performance. In this paper, multiple lock-in frequency modulated thermography is applied to investigate defects on the subsurface of polypropylene/glass fiber composites, and experimental investigations were conducted on various defect conditions for qualitative and quantitative estimation. A thin rectangle-shaped PP-GF plate of dimension 100 × 180 mm and a depth of 3 mm with various 24 defects conditions was considered as a test sample. The sample was stimulated at multiple modulation frequencies, viz., 1 Hz, 0.2 Hz, 0.1 Hz, 0.05 Hz, 0.03 Hz, and 0.01 Hz. For qualitative and quantitative evaluation, two representatives, multiple lock-in frequency modulated algorithms of lock-in thermography, HA and FFT, were applied to the raw data. Moreover, their detectability performance was compared in terms of phase contrast and signal-to-noise ratio.

The RCNDE industrial members' 2022 vision for the future requirements of NDE

The UK Research Centre in Non-Destructive Evaluation (RCNDE), established in 2003, has become a leading industrial-academic collaboration for undertaking industrially relevant research in the fields of non-destructive evaluation (NDE) and structural health monitoring (SHM). The industrial members produced their first five-, ten- and 20-year vision for NDE in 2011 and updated it in 2016. This article describes the newly updated vision for NDE that was produced by the industrial members in 2021/2022. The overall vision presented in this article is a consolidation of the individual industrial sector visions, a process that is possible due to the fact that many longer-term objectives are common across sectors. This enables research projects to be defined that appeal to multiple industries and demonstrates the power of collaboration to deliver relevant solutions. To aid understanding, the responses have been grouped into three themes: improvements desired in the capabilities of NDE technologies; asset-based inspection challenges that need to be met; and aspects that deliver and help to realise the benefits of Industry 4.0. The vision produced by RCNDE's industrial members will be used to shape the core research programmes and identify opportunities for new collaborations. It will help to bring forward the application of inspection and monitoring technologies to meet future industrial needs and timescales.

Nondestructive testing and evaluation techniques of defects in fiber-reinforced polymer composites: A review

Fiber-reinforced polymer composites have excellent mechanical properties and outstanding development potential and are cost-effective. They have increasingly been used in numerous advanced and engineering applications as materials for wind turbine blades, helicopter rotors, high-pressure pipelines, and medical equipment. Understanding and assessing structural failure promptly in the whole lifecycle of a composite is essential to mitigating safety concerns and reducing maintenance costs. Various nondestructive testing and evaluation (NDT&E) technologies based on different evaluation principles have been established to inspect defects under different conditions. This paper reviews the established types of NDT&E techniques: acoustic emission, ultrasonic testing, eddy current testing, infrared thermography, terahertz testing, digital image correlation, shearography, and X-ray computed tomography, which is divided into three categories based on the operation frequency and data processing means of the output signal that is directly under analysis. We listed four types of defects/damage that are currently of great interest, namely, voids and porosity, fiber waviness and wrinkling, delamination and debonding, as well as impact damage. To identify a suitable method for different defects/damage, we performed characterization and evaluation by using these NDT&E techniques for typical defects/damage. Then, the cost, inspection speed, benefits and limitations, etc. were compared and discussed. Finally, a brief overview of the development of the technologies and their applications in the field of composite fabrication was discussed.

Evaluating Log Stiffness Using Acoustic Velocity for Manufacturing Structural Oriented Strand Board

Oriented strand board (OSB) is an engineered panel product formed by layering strands of resinated wood in specific orientations into a mat, then pressing the mat at a high temperature to form a panel of desired strength and stiffness. OSB manufacturing facilities utilize small diameter logs from thinning operations and waste from harvesting. Considerable variation exists in the wood properties of the raw material and ideally the OSB industry would take advantage of such variation, however, it lacks the technology required to rapidly assess log quality on-site. Non-destructive evaluation (NDE) techniques based-on acoustics have the potential to rapidly segregate logs in the field, however the influence of acoustic-based log segregation on OSB panel properties is unknown. The aims of this project were to determine if log quality affects panel properties and if acoustic NDE technology is a satisfactory tool for determining log stiffness prior to entering the manufacturing process. It was found that low velocity (stiffness) logs produced panels with low stiffness while high and medium velocity (stiffness) logs produced panels with similar properties. The Director HM 200 was a satisfactory tool for determining log stiffness. Further studies are required to determine how to incorporate NDE tools into the manufacturing process.

Binarization Mechanism Evaluation for Water Ingress Detectability in Honeycomb Sandwich Structure Using Lock-In Thermography.

The growing use of composite honeycomb structures in several industries including aircraft has demonstrated the need to develop effective and efficient non-destructive evaluation methods. In recent years, active thermography has attracted great interest as a reliable technology for non-destructive testing and evaluation of composite materials due to its advantages of non-contact, non-destructive, full-area coverage, high speed, qualitative, and quantitative testing. However, non-uniform heating, low spatial resolution, and ambient environmental noise make the detection and characterization of defects challenging. Therefore, in this study, lock-in thermography (LIT) was used to detect water ingress into an aircraft composite honeycomb sandwich structure, and the phase signals were binarized through the Otsu algorithm. A square composite honeycomb with dimensions of 210 mm × 210 mm along with 16 different defective areas of various sizes in groups filled with water by 25%, 50%, 75%, and 100% of the cell volume was considered. The sample was excited at multiple modulation frequencies (i.e., 1 Hz to 0.01 Hz). The results were compared in terms of phase contrast and CNR according to the modulation frequency. In addition, the detectability was analyzed by comparing the number of pixels of water ingress in the binarized image and the theoretical calculation.

Air-coupled ultrasonic assessment of concrete rail ties

This paper investigates a contactless non-destructive evaluation technology for fast and efficient condition assessment of concrete rail ties. Ultrasonic surface waves are generated in rail ties using an air-coupled ultrasonic transmitter and are sensed at another location along the tie using an array of micro-electromechanical system (MEMS) sensors. Concrete rail ties with different types of damage are evaluated. The measured signal data are visualized in time-space (t-x) and frequency-wavenumber (f-k) domains, and four signal parameters are derived. Signal parameters measured from the damaged ties are compared with those from undamaged/healthy ties to generate two- and four-dimensional decision spaces. Threshold decision boundaries separating healthy and damaged rail ties in the decision space are computed using the support vector machine (SVM) algorithm, and the ability of these decision spaces to distinguish between healthy and damaged rail ties is investigated using statistical data analysis. The results demonstrate that high quality signal data are obtained from concrete rail ties using the proposed air-coupled configuration. The four-dimensional decision space, which uses all four signal parameters together, can distinguish between healthy and damaged rail ties with accuracies of around 80 %.

Study on Nondestructive Testing and Evaluation Technology for Service Performance of Existing Bridge Pile

For existing bridge piles where the superstructure has been built, traditional pile test methods are difficult to apply. In this paper, a nondestructive testing and evaluation technology for service performance of existing pile is put forward. Based on a reinforcement case of existing municipal bridge, the nondestructive method (parallel seismic wave method) is applied to detect pile defects and bearing capacities. In the field test, two excitation types are compared to acquire better stress wave signal quality. The result shows that the length and structural defects of piles can be detected by the parallel seismic wave method. The vertical excitation at bridge superstructure is more conducive to strengthen the first arrival wave characteristics. And the bearing capacity of existing piles can be further estimated based on the soil stratum conditions revealed by the test boreholes and the given survey parameters.

Fusion and Visualization of Bridge Deck Nondestructive Evaluation Data via Machine Learning

To maintain infrastructure safety and integrity, nondestructive evaluation (NDE) technologies are often used for detection of subsurface defects and for holistic condition assessment of structures. While the rapid advances in data collection and the diversity of available sensing technologies provide new opportunities, the ability to efficiently process data and combine heterogeneous data sources to make robust decisions remains a challenge. Heterogeneous NDE measurements often conflict with one another and methods to visualize integrated results are often developed ad hoc. In this work, we present a framework to support fusion of multiple NDE techniques in order to improve both detection and quantification accuracy while also improving the visualization of NDE results. For data sources with waveform representations, the discrete wavelet transform (DWT) is used to extract salient features and facilitate fusion with scalar-valued NDE measurements. The description of a signal in terms of its salient features using a wavelet transform allows for capturing the significance of the original data, while suppressing measurement noise. The complete set of measurements is then fused using nonparametric machine learning so as to relax the need for Bayesian assumptions regarding statistical distributions. A novel visualization schema based on classifier confidence intervals is then employed to support holistic visualization and decision making. To validate the capabilities of the proposed methodology, an experimental prototype system was created and tested from NDE measurements of laboratory-scale bridge decks at Turner-Fairbank highway research center (TFHRC). The laboratory decks exhibit various types of artificial defects and several non-destructive tests have already been carried out by research center technicians to characterize the mechanical properties of the materials and the existing damages. The behaviors of several different data fusion approaches are compared here. Based on the presented analyses, it is demonstrated that fusion via support vector machines provided the most robust and consistent data fusion and defect detection capabilities.