ABSTRACTIn order to develop methods for diagnosing the stress-strain state of steel products in view of their history in the form of cyclic loading we study the effect of previous zero-to-tension cyclic loading on the magnetic behavior of the 08G2B steel under subsequent static elastic tension along the same direction. Magnetic measurements were made both in a closed magnetic circuit and by means of attached magnetic devices along and across the tension axis. The history in the form of previous cyclic tension affects the behavior of the magnetic parameters of the material under subsequent elastic static tension. Particularly, the growing number of preloading cycles is accompanied by an increase in the magnitude of applied static stresses at which there are extrema of the magnetic characteristics measured longitudinally. This shift of the extrema of the magnetic parameters is explained by residual compressive stresses increasing with the number of previous tension cycles. At applied tensile stresses ranging between 0 and 100 MPa, the magnetic characteristics measured longitudinally on specimens cyclically loaded with various numbers of cycles vary uniquely. The difference in the values of the coercive force measured longitudinally and crosswise decreases monotonically at applied stresses ranging between 0 and 200 MPa.

ABSTRACTThe increasing use of stainless steel in industrial structures can be attributed to its excellent mechanical properties at elevated temperatures. Martensitic grade stainless-steel is used, for example, to manufacture steam turbine blades in power plants. The failure of these turbine blades can result in equipment damage contributing to expensive plant failures and safety concerns. Degradation and structural failure of these blades is largely attributed to material fatigue, at the microstructure level. Hence, it is important to evaluate the level of fatigue prior to the initiation of macro defects to ensure the viability of these components. Conventional nondestructive evaluation (NDE) techniques such as ultrasonic testing and eddy current testing are suitable in detection of macro defects such as cracks, but not very effective in evaluating degradation of the material at a microstructure scale. This article investigates the feasibility of the nonlinear eddy current (NLEC) technique to detect fatigue in martensitic grade stainless-steel samples along with a methodology to classify the samples. K-medoids clustering algorithm and genetic algorithm are used to classify the samples according to the severity of fatigue. Initial results indicate that stainless-steel samples, in different stages of fatigue, can be classified into broad categories of low, mid, and high levels of fatigue.

ABSTRACTAdditive manufacturing (AM) remains to be a rapidly growing industry with applications that are extended beyond metals and to other materials, such as polymers, ceramics, and concrete, to name a few. However, advancement in the development of inspection techniques, particularly in-line nondestructive testing (NDT) methods, lags significantly. Most of the research in developing such methods has focused on metal-based AM. This paper investigates the efficacy of three high-resolution near-field millimeter-wave probes for detecting small voids in the feedstock polymeric filaments used for AM. The electromagnetic (EM) design and optimization of these probes are discussed in this paper. The design of the probes is based on concentrating the interrogating electric field of an open-ended waveguide in a small region corresponding to the area of a thin dielectric slab insert. This results in achieving a higher spatial resolution than when using only the open-ended waveguide. Extending the dielectric slab to an optimum value out of the waveguide makes the electric field more concentrated and potentially further improves the spatial resolution. These modifications also reduce the detection sensitivity as a function of increasing standoff distance. However, the spatial resolution of these probes varies more rapidly as the standoff distance increases. Subsequently, the efficacy of these three probes was studied and compared using a comprehensive set of numerical EM simulations at V-band (50–75 GHz). Afterward, three such probes were fabricated, at V-band (50–75 GHz), and were used to measure the reflection responses of the stock Polylactic Acid (PLA) filaments with a very small hemispherical surface void. Root-Mean-Squared-Error (RMSE), between reference and defective filaments and over the simulated and measured frequency range, was calculated as a criterion to compare the detection capability of the three probes in the entire frequency band. The results showed that at V-band (50–75 GHz) the spatial resolution of the standard open-ended rectangular waveguide is deemed sufficient detecting small surface voids of the stock PLA filaments.

ABSTRACTThis paper reports a laboratory implementation of a prototype sensor for the measurement of in situ concrete static modulus of elasticity. Sensor measurements for a trial concrete mixture were generated, post-processed, and calibrated to best match companion ASTM C469 modulus of elasticity testing performed on the same mixture for concrete ages up to 28 days. The sensor prototype measurements demonstrated close agreement with ASTM C469 testing with approximately 90% of calibrated sensor prototype measurements within ±5% of companion testing. The initial sensor prototype implementation reported in this study affirms the feasibility of in situ concrete modulus of elasticity measurement and justifies further sensor development and implementation efforts.

ABSTRACTIn order to improve the efficiency and accuracy of corroded cracks detection and classification in reinforced concrete, a corroded cracks identification model Steel Corrosion Net (SCNet), based on deep learning Convolutional Neural Network (CNN), is proposed. Crack figures are collected by self-shooting, internet search and corrosion test, then the data set of 39,000 pictures is built by data enhancement. Afterward, a SCNet three-classification neural network model is built and tested using TensorFlow learning framework and Python. The SCNet combines massive initial data with a multi hidden layer neural network framework, and achieves feature learning and accurate classification through model training. According to the training and testing accuracy of the model, the structure and parameters of the SCNet network are optimized. The results of SCNet are compared with those obtained by two traditional testing methods. The results show that the proposed SCNet model achieves a classification accuracy of 96.8%, so it can effectively identify and classify the corroded cracks in reinforced concrete, with high accuracy and measurability. Under harsh condition of noise interference, such as shadows and distortions, the proposed SCNet model shows a relatively stable classification performance compared with two traditional methods.

ABSTRACTA laser ultrasound system is integrated into a directed energy deposition additive manufacturing (DED-AM) chamber to use Rayleigh waves for process monitoring in a noncontact layer-by-layer mode. Layers of Ti-6Al-4 V are deposited and then interrogated with ultrasonic Rayleigh waves that are sensitive to flaws and material nonuniformities. The novel integrated material processing and monitoring system is described in detail. Process parameters are intentionally altered to create flaws and anomalies to demonstrate some capabilities of the monitoring system. The generation laser actuates either broadband pulses with a cylindrical lens or narrowband wave packets with a slit mask, which are received in through-transmission mode by a laser interferometer despite the inherent surface roughness. Flaws are detected through comparison to a reference state.

ABSTRACT Industry 4.0 (I4.0) describes the current revolution of the industrial world with a strong impact on the complete production sector. Data about production processes and the corresponding material and product status are the key elements. All over the world, the protagonists of I4.0 are facing the challenges to define appropriate concepts for I4.0 infrastructure, data exchange, communication interfaces and efficient procedures for the interaction of I4.0 elements. The role of future Nondestructive Evaluation (NDE4.0) and corresponding workflows (i.e. data generation and evaluation) will change accordingly. Thus, NDE4.0 systems will be elements of the Industrial Internet of Things (IIoT) that communicate with the production machines and devices. They become an integral part of the digital production world and the industrial data space. This paper is a summarized overview of our current developments as well as of general key technologies and future challenges to enable the paradigm change from classical NDT toward NDE4.0, starting with approaches on signal processing, artificial intelligence-based information generation and decision making, generic data formats and communication protocols. For illustration purposes, prototypical implementations of our work are presented. This includes a pilot development of a modern human- machine-interaction by the use of assistance technologies for manual inspection.

ABSTRACTThis paper presents an inspection technique based on highly nonlinear solitary waves, wireless transducers, and machine learning. The technique was demonstrated on a plate subjected to accelerated corrosion while monitored with wired and wireless transducers, designed and assembled in laboratory. The tethered device consisted of a chain of spheres surmounted by a solenoid wired to and driven by a data acquisition system to control the first particle of the chain in order to induce the impact between the particle and the chain needed to generate the stress wave. The chain contained a piezoelectric wafer disk, also wired to the same data acquisition system, to sense the waves. The wireless transducers were identical to their wired counterparts, but the data acquisition system was replaced by a wireless node that communicated with a tablet via Bluetooth. Four wired and four wireless transducers were used to monitor the plate for nearly a week to detect the onset and progression of electrochemical corrosion. A few features were extracted from the time waveforms and then fed to a machine learning algorithm to classify damage. The results showed the effectiveness of the proposed approach at labeling defects close to the transducers.

Dear Reader, This Special Issue of the Research in Nondestructive Evaluation is devoted to the recently introduced term NDE 4.0, which is a natural part of actively ongoing activities during the la...

ABSTRACTThe use of machine-learning based algorithms on a large-scale nondestructive evaluation (NDE) data considerably advances the NDE techniques toward complete industrial automation. In this article, simultaneous feature selection and feature weighting are carried out on the magnetic Barkhausen emission (MBE) dataset to demonstrate the significance of optimization in NDE data. Antlion optimization is employed as a searching method to determine the optimum feature set that will maximize the classification performance. The proposed framework is validated for different magnetization frequencies separately and found to be frequency independent. The framework resulted in the selection of four significant features extracted from the MBE response thereby reducing the computational effort and improving the accuracy to 98.4% for AdaBoost classifier. The developed machine learning methodology is a potential strategy for processing industrial sensory data since material testing, property prediction, and categorization are frequent tasks in manufacturing and production engineering industries. Further, this research demonstrated the necessity of embedded intelligence in automation of NDE toward Industrial Revolution 4.0.

ABSTRACTThe detection and evaluation of corrosion defects take on a critical significance to ensure the service safety of steel structures in civil engineering. The quantitative evaluation of corrosion defects has not been well addressed though metal magnetic memory (MMM) testing technology has been investigated in steel corrosion problems. In this study, the Q345qD steel plates were taken as the specimens of MMM testing. Specimens with different corrosion degrees were developed through electrochemical corrosion, and the change laws of the MMM signals and the characteristics of different corrosion specimens were analyzed. A three-dimensional (3D) magnetic charge model of the corrosion area was built based on the magnetic charge theory, such that the change laws of the MMM signal in the corrosion area from the mechanism were explained. The finite element simulation results of the corrosion specimens were well consistent with the experimental and theoretical results. A quantitative evaluation method for corrosion defect depth was proposed in combination with finite element simulation and experimental data. Comparing the experimental data and the inversion data, the relative errors of the determined defect depth h were within 20%, suggesting that the proposed evaluation method is feasible for the quantitative evaluation of steel corrosion depth.

ABSTRACTNondestructive testing (NDT) of thermal barrier coatings (TBCs) is a critical and ongoing topic of research and development. In particular, inspection techniques that determine the thickness of ceramic topcoat and thermally grown oxide (TGO) are of interest. This work investigates the utility of open-ended rectangular waveguide probes in the millimeter wave frequency range of 26.5–110 GHz for evaluation of topcoat and TGO thicknesses through a compressive set of electromagnetic (EM) simulations. In addition, these EM simulations are used to illustrate the influence of probe size and TBC substrate curvature on the complex reflection coefficient properties and the subsequent thickness estimation. The impact of volumetric porosity level on the same is also investigated. A standing-wave probe at V-band (50–75 GHz) is constructed and used to measure the topcoat thickness on three button-type TBC samples. This probe eliminates the need for using expensive and bulky vector network analyzers (VNA), which is quite desirous from a practical point-of-view. The experimental results indicate the capability of estimating the topcoat thickness to within ±15 μm (0.6 mils).

ABSTRACTA novel nondestructive testing method for subsurface defects detection in thin composite structures using dynamic laser speckles is proposed. In this method, a laminated composite panel containing a subsurface defect is excited by a frequency scanned ultrasonic (US) wave and is illuminated by an expanded laser beam. If one of resonant frequencies of the defect coincides with the US frequency, a local area (a region of interest or ROI) of the panel optically rough surface, placed directly above the defect, begins to vibrate, and the sequences of difference speckle patterns containing the spatial response from the defect are recorded. The formation of this response is caused by both decorrelation and speckle blurring within the local speckle pattern generated by the vibrating ROI at its opposite tilts. The accumulation of difference speckle patterns increases the intensity of the spatial response. This method differs from similar ones in that defects are detected using dynamic speckle patterns of a composite rough surface, illuminated by a single expanded laser beam. The verification of the proposed method was performed using a hybrid optical-digital experimental breadboard to test composite panels containing artificial subsurface defects, as well as a real defect.

ABSTRACTA metal clad plate consists of a base layer and a clad layer of different materials. When testing clad plate using the eddy current (EC) method, the variation of EC density along the thickness direction in clad plate is complicated owing to the interface between the layers. This article attempts to investigate the characteristics of the variation of EC density in the thickness direction in clad plate based on finite element analysis. The results are then used for revealing the mechanism of the relation between coil voltage and the thickness of the clad layer which is essential for the measurement of cladding thickness. Thereupon, the experiment of measuring cladding thickness is performed, in which the testing frequency is selected based on the voltage-thickness relation. It turns out that the measurement is accurate and the results have good linearity.

ABSTRACT This study explores the application of algorithms with linear array ultrasonic testing for the characterization of hidden regions of impact damage in composites. An idealized ray tracing model was used to demonstrate the sensitivity of transmitted signals to the hidden impact profile, and a numerical model was used to provide insight on the incident field generated by linear array elements. Experimental studies were performed highlighting the differences in the response from no flaw, columnar and trapezoidal profiles. Algorithms were implemented to process full matrix capture data, register pitch-catch signals with the top delamination location and extent, and improve the signal-to-noise through combining multiple pitch-catch acquisitions. Lastly, a classifier was developed and verification testing demonstrated the ability to distinguish four different hidden profiles, indicating the importance of signal registration for successful classification.

ABSTRACTCarbon fiber reinforced polymer (CFRP) laminates are extensively used in aviation due to their excellent material properties. Damages resulted from manufacturing or usage seriously undermine the safety of aircraft structures. Therefore, the efficient detection of the damage in CFRP laminates is important. In this paper, three kinds of common damages including impact damage, crack damage and delamination damage are detected based on pulsed eddy current (PEC) with rectangular differential sensors. Firstly, to overcome shortcomings of traditional PEC sensors, an optimized rectangular differential sensor is proposed in the study. Then, a normalization method is introduced to process the differential signal obtained with the sensor. Finally, the relationship between the size of the damage and the normalized differential signal is investigated. The investigation results show that the rectangular differential sensor can detect the three types of damages. When a new damage object is detected, the measurement step of a reference signal is omitted. The peak value of the normalized differential signal increases with the increase in damage size. The new PEC method has been proved in the study.

ABSTRACTUpon exciting a material using elastic waves, the locally induced deformation at the interfaces of internally closed defects may cause nonlinear wave mechanics and dynamics in the form of clapping and friction. As a result, both phenomena instigate spectral broadening of the excitation spectrum as well as the production of heat, directly originating from the defect. To better understand and account for the physics behind the dissipation of energy by internally closed defects as a result of the wave–interface interaction, dedicated models can be developed. In this work, we propose a 3D finite element multiphysics model that is capable of simultaneously describing the generation of nonlinearities and heating at a defect’s interface experiencing clapping and friction induced by elastic wave propagation. The model consists of three different modules. The first module describes the elastic wave propagation in a virgin/bulk material, whereas the second module captures the contact physics at the defect level. The third module is implemented to calculate the diffusion of thermal energy in the specimen. The contact physics module accounts for anharmonic and hysteretic effects, describing the nonlinear behavior of the defect’s interfaces, which is echoed in both the ultrasound spectrum and in the vibration-induced heating. A qualitative analysis of the computational model, integrating the three modules, is performed to validate the approach. Examples show that nonlinear spectral components are indeed observed as a result of the friction and the clapping experienced by the faces of the defect. At the same time, a localized temperature increase due to the induced friction is noted, and its response at the outer surface of the sample is examined. The qualitative validation approves that the model is ready to be tested further quantitively, and to compare its predictions to experiments.

ABSTRACTFundamental elements of wave mechanics are covered with respect to Acoustic Emission (AE) analysis in Nondestructive Evaluation (NDE) and Structural Health monitoring (SHM). Emphasis is placed on aspects of Ultrasonic Guided Waves that travel in a structure due to elastic wave emissions from a defect source as defects continue to grow. The AE Method is based on the emission of elastic waves from a particular source as failure is initiated – crack, corrosion, leak, and so on. The AE signal received is a function of the source orientation and location as well as wave velocities in the structure, sensor types and positions, arrival time differences, thicknesses of the structure, and specimen structural variations. Such topics as Guided wave physical and theoretical considerations, Ultrasonic Guided wave accomplishments, and enhanced AE by considering various guided wave concepts are discussed. The topic of Acousto-Ultrasonics and its impact on guided wave understanding is also reviewed. This paper illustrates how principles in Ultrasonic Guided waves can be applied to Acoustic Emission. Besides basic issues, Acoustic Emission enhancement possibilities are based on recent studies of Ultrasonic Guided Waves and the use of Shear Horizontal guided waves in Acoustic Emission along with an omni-directional shear horizontal wave transducer.Editor’s note: Joseph L. Rose, PhD, is the recipient of the 2021 ASNT Research Recognition for Sustained Excellence. Rose presented on “Guided Wave Studies for Enhanced Acoustic Emission Inspection” during the ASNT Research Symposium which was held 27–29 April 2021.

ABSTRACT In the UK, the NDE community is making a coordinated effort to underpin and enable the full benefits of the large-scale trend toward comprehensive digitalization and automation of industrial processes and assets, frequently referred to as “Industry 4.0”. Certain facets of what is now considered to be NDE 4.0 have been the subject of research for some time and have already gained industrial traction, while others are quite new, with unexplored potential and pitfalls. However, in these areas there is scope for learning from progress in fields outside of, but related to, NDE such as dimensional metrology. This paper reviews progress to date based on UK activities, considers some planned and potential research tasks in this domain, and highlights the major challenges the NDE community must tackle. In particular, as interoperability and data reuse are key features of Industry 4.0 practices, international and cross-domain efforts on data format standardization are needed. It is clear that, without the NDE community stepping up to the challenge, much of Industry 4.0 cannot be realized; yet if the NDE 4.0 vision is implemented comprehensively, NDE has the potential to become more capable, more valuable, and therefore more highly valued.

ABSTRACT Industrial radiography is considered as one of the most important nondestructive testing methods for different inspections. The radiography images often have a poor signal-to-noise ratio mainly because of the scattered X-rays. Image processing methods may be used to enhance the contrast of radiographs for better defect detection. In this study, outcomes from three total variations (TV) based methods were analyzed and compared. Implemented algorithms were ROF-TV, non-convex p-norm total variation (NCP-TV) and non-convex logarithm-based total variation (NCLog-TV). These TV-based methods have been implemented indirectly as high pass edge-enhancing filters. Based on qualitative operator perception results, the study has shown that the application of all three methods resulted in improved image contrast enabling enhanced image detail visualization. Subtle performance differences between the outputs from different algorithms were noted, however, especially around the edges of image features. Furthermore, it was found that all implemented algorithms have similarities in performance, generate approximately the same results and are suitable for weld inspection.