Martensitic grade stainless steel is generally used to manufacture steam turbine blades in power plants. The material degradation of those turbine blades, due to fatigue, will induce unexpected equipment damage. Fatigue cracks, too small to be detected, can grow severely in the next operating cycle and may cause failure before the next inspection opportunity. Therefore, a nondestructive electromagnetic technique, which is sensitive to microstructure changes in the material, is needed to provide a means to estimate the specimen’s fatigue life. To tackle these challenges, this paper presents a novel Magnetic Barkhausen noise (MBN) technique for garnering information relating to the material microstructure changes under test. The MBN signals are analyzed in time as well as frequency domain to infer material information that are influenced by the samples’ mate- rial state. Principal Component Analysis (PCA) is applied to reduce the dimensionality of feature data and extract higher order features. Afterwards, Probabilistic Neural Network (PNN) classifies the sample based on the percentage fatigue life to discover the most correlated MBN features to indicate the remaining fatigue life. Furthermore, one criticism of MBN is its poor repeatability and stability, therefore, Analysis of Variance (ANOVA) is carried out to analyze the uncertainty associated with MBN measurements. The feasibility of MBN technique is investigated in detecting early stage fatigue, which is associated with plastic deformation in ferromagnetic metallic structures. Experimental results demonstrate that the Magnetic Barkhausen Noise technique is a promising candidate for characterizing.

Ultrasonic non-destructive testing traditionally uses a conventional monolithic transducer. An approach similar to this comprising of independent single transmissions but with reception performed by all the elements in phased array ultrasonics is known as Full Matrix Capture (FMC). The acquired data is processed by Total Focusing Method (TFM). Conventional FMC-TFM has limitations in the inspection at large depth in attenuating materials due to single element transmission. To improve the beam forming process, coherent recombination of the plane wave with specific angles is utilized in transmission and the same aperture is used for the reception in Plane Wave Imaging (PWI). A new methodology called Angle Beam Virtual Source FMC-TFM (ABVSFMC-TFM) is proposed to inspect thick attenuating materials such as nickel base alloys. The ABVSFMC method leads to improved Signal to Noise Ratio (SNR) as compared to the conventional FMC due to increased energy with directivity during transmission using a group of elements and improved divergence as compared to the PWI due to a small virtual source near the sample surface. In the present paper, FMC-TFM, PWI-TFM and ABVSFMC-TFM methods are compared for inspection of thick nickel base superalloy (Alloy 617) with slots at various depths in the range of 25-200 mm. Optimization of the incidence angle has been performed by beam computation in CIVA software. Results obtained by CIVA simulations are discussed and also compared for the three methods.

An important advantage of guided waves is their ability to propagate large distances and yield more information about flaws than bulk waves. Unfortunately, the multi-modal, dispersive nature of guided waves makes them difficult to use for locating flaws. In this work, we present a method and experimental data for removing the deleterious effects of multi-mode dispersion allowing for source localization at frequencies comparable to those of bulk waves. Time domain signals are obtained using a novel 64-element phased array and processed to extract wave-number, and frequency spectra. By an application of Auld's reciprocity theorem, mode amplitudes are extracted approximately using a variational method. Once mode contributions have been obtained, the dispersion for each mode can be removed via back-propagation techniques. Excepting the presence of a small artifact at high frequency-thickness products, experimental data successfully demonstrates the robustness and viability of this approach to guided wave source location.

In Guided Wave Structural Health Monitoring (GW-SHM), a strong need for reliable and fast simulation tools has been expressed throughout the literature in order to optimize SHM systems or demonstrate performance. Even though guided wave simulations can be conducted with most finite elements software packages, computational and hardware costs are always prohibitive for large simulation campaigns. A novel SHM module has been recently added to the CIVA software and relies on unassembled high order finite elements to overcome these limitations. This paper focuses on the thorough validation of CIVA for SHM to identify the limits of the models. After introducing the key elements of the CIVA SHM solution, a first validation is presented on a stainless steel pipe representative of the oil and gas industry. Second, validation is conducted on a composite panel with and without stiffener representative of some structures in the aerospace industry. Results show an excellent match between the experimental and simulated datasets, but only if the input parameters are fully determined prior to the simulations.

Typically, in a reactor-coolant fatigue test loop, an autoclave used for housing the test specimen. For the purpose, often a small tubular autoclave used. This is for reducing the cost of building the test loop and for avoiding rigorous ASME pressure vessel qualification criterion as required for qualification of a larger high-pressure-temperature vessel. However, use of a small autoclave along with high-pressure flow inside the autoclave, don't allow to put an extensometer (inside the autoclave) for measuring the strain. Measurement of strain during the fatigue test of a specimen is required to assess the accuracy of the test and for downstream activities such as for stress-strain based material model developments, stress analysis validation, etc. In this paper, we discuss an artificial-intelligence and machine-learning framework for predicting time-series strain from other sensor measurements such as from load cell, frame pull-rod displacement, actuator displacement sensors. First the framework was trained and validated against in-air condition fatigue test data (for which the strain was measurable). Then the validated model was used for predicting strain in a reactor-coolant fatigue test loop, in which direct strain was not measurable. The original aim of the research was to improve the mechanical testing and measurements capability for conducting fatigue tests in a high-temperature-pressure reactor-coolant flow environment. However, similar approach can be used for predicting strain in an actual reactor component.

In view of their higher sensitivity in localizing an incipient damage, methods of NDE based on the nonlinear wave-damage interactions have been of continued interest. In this paper, the propagation of guided waves through a delamination with contacting interfaces is studied numerically using a finite element based framework. In particular, influence of the interlaminar location of the delamination on the nonlinear acoustic features in the response spectrum is investigated in detail. The numerical framework is validated by an in-house experimentation performed on a unidirectional GFRP laminate containing a through-width delamination. A parameter, referred to as the nonlinearity index, is defined for quantifying the strength of the nonlinear wave-damage interactions and its dependence on the interlaminar location of the delamination is studied across a range of interrogation frequencies. The notion of contact energy intensity is introduced and further used for justifying the trends of variation of the nonlinearity index obtained numerically and observed experimentally. Results indicate that two fundamental parameters govern the underlying contact phenomenon; they are, the phase difference between the wave packets passing through the two sub-laminates and the flexural rigidities of the two sub-laminates present at the site of the delamination defect. While the former controls the relative displacement between the two sub-laminates, the latter governs the propensity of collisions between the two sub-laminates. Finally, a diametric effect of these two parameters on the generation of nonlinear harmonic signals with varying interlaminar location of the delamination is brought out.

Sonic infrared (SIR) imaging is an original hybrid nondestructive evaluation (NDE) technique that has seen rapid acceptance in the industry. A single-tone ultrasonic wave in the 15–40 kHz range is induced to the specimen under inspection through a high-power ultrasonic plastic welder. Heating duration is equal to the ultrasonic excitation duration. In a previous article, an analytical model for depth profiling using SIR NDE was presented. According to the proposed model, material thermal properties, defect size and ultrasonic excitation duration influence defect characterization and contribute to the total temperature-time curves. In this paper, heating duration effect on the quantitative estimation of flaws using sonic infrared nondestructive evaluation was investigated.

A novel failure model updating methodology is presented in this paper for composite materials. The innovation in the approach presented is found in both the experimental and computational methods used. Specifically, a dominant bottleneck in data-driven failure model development relates to the types of data inputs that could be used for model calibration or updating. To address this issue, nondestructive evaluation data obtained while performing mechanical testing at the laboratory scale are used in this paper to form a damage metric based on a series of processing steps that leverage raw sensing inputs and provide progressive failure curves that are then used to calibrate the damage initiation point computed by full-field three-dimensional finite element simulations of fiber-reinforced composite material that take into account both intra- and interlayer damage. Such curves defined based on nondestructive evaluation data are found to effectively monitor the progressive failure process, and therefore, they could be used as a way to form modeling inputs at different length scales.

Electronics operating at cryogenic temperatures play a critical role in future science experiments and space exploration programs. The Deep Underground Neutrino Experiment (DUNE) uses a cold electronics system for data taking. Specifically, it utilizes custom-designed Application Specific Integrated Circuits (ASICs). The main challenge is that these circuits will be immersed in liquid Argon and that they need to function for 20+ years without any access. Ensuring quality is critical, and issues may arise due to thermal stress, packaging, and manufacturing-related defects: if undetected, these could lead to long-term reliability and performance problems. This paper reports an investigation into non-destructive evaluation techniques to assess their potential use in a comprehensive quality control process during prototyping, testing, and commissioning of the DUNE cold electronics system. Scanning acoustic microscopy (SAM) was used to investigate permanent structural changes in the ASICs associated with thermal cycling between room and cryogenic temperatures. Data are assessed using a correlation analysis, which can detect even minimal changes happening inside the ASICs.

Pipelines are the primary means of land transportation of oil and gas globally, and pipeline integrity is, therefore, of high importance. Failures in pipelines may occur due to internal and external stresses that produce stress concentration zones, which may cause failure by stress corrosion cracking. Early detection of stress concentration zones could facilitate the identification of potential failure sites. Conventional non-destructive testing (NDT) methods, such as magnetic flux leakage, have been used to detect defects in pipelines; however, these methods cannot be effectively used to detect zones of stress concentration. In addition, these methods require direct contact, with access to the buried pipe. Metal magnetic memory (MMM) is an emerging technology, which has the potential to characterize the stress state of underground pipelines from above ground. The present paper describes magnetic measurements performed on steel components, such as bars and tubes, which have undergone changing stress conditions. It was observed that plastic deformation resulted in the modification of measured residual magnetization in steels. In addition, an exponential decrease in signal with the distance of the sensor from the sample was observed. Results are attributed to changes in the local magnetic domain structure in the presence of stress but in the absence of an applied field.

Composites are being increasingly used in various industries due to their lower cost and superior mechanical properties over traditional materials. They are nevertheless vulnerable to various defects during manufacturing or usage which can cause failure of critical engineering structures. Hence, there is a growing need for nondestructive evaluation (NDE) of composites to detect such defective structures and avoid significant loss and damages. Microwave NDE has several advantages over other existing NDE techniques for detecting defects or faults in non-conducting composites or dielectrics. One of the primary benefits of microwaves is large probe-standoff distances which allow for rapid scan times. However, the resolution of such far-field microwave sensors is diffraction limited. Metamaterial-based lens, also known as “superlens,” can achieve resolution beyond the diffraction limits due to its unique electromagnetic (EM) properties. This contribution focuses on the physical design of a metamaterial lens. The theory underlying the design of a metamaterial lens is presented followed by simulation and experimental results. This paper also investigates the feasibility of using the metamaterial lens for improving the resolution of microwave imaging in NDE of composites.

Honeycomb sandwich structures (HSS) are widely used in the aerospace industry due to their high strength-to-stiffness ratio. However, these materials are susceptible to damage during manufacturing or service that can cause great loss in the load bearing capacity or even failure. Thus, periodic or continuous nondestructive evaluation (NDE) of HSS is essential for safe operation. Development of effective NDE technique is challenging due to the geometric complexity of the honeycomb core. Guided ultrasonic waves are ideal for large-scale testing because of their large propagation range and high sensitivity to defects in their path. In this paper, an improved NDE method for detecting disbonds at the top and bottom interfaces between the core and facesheets is proposed based on experimental studies. By applying excitation signals at different frequencies, the responses at the top and bottom surface of plate-like HSS component are compared and analyzed. The response in a specific frequency range is further studied by introducing disbonds at the top interface. It is shown that some components of the recorded signal in a specific frequency range are more sensitive for detecting the disbond. In addition, an improvement of the conventional damage index based on the damage feature is proposed, and a systematic procedure for detecting damage inside HSS is conducted on an elevator section of an Airbus 330. The results show that the optimized damage index greatly improves the resolution and adaptability of damage detection in the structures.

Robust defect detection in the presence of grain noise originating from material microstructures is a challenging yet essential problem in ultrasonic non-destructive evaluation (NDE). In this paper, a novel method is proposed to suppress the gain noise and enhance the defect detection and imaging. The defect echo and grain noise are distinguished through analyzing the spatial location where the echo is originating from. This is achieved by estimation of the angle of arrival (AOA) of the returned echo and evaluation of the likelihood that the echo is reflected from the point where the array is focused or otherwise from the random reflectors like the grain boundaries. The method explicitly addresses the statistical models of the defect echoes and the spatial noise across the array aperture, as well as the correlation between the flaw signal and the interfering echoes; estimates the AOA and the likelihood in a dimension-reduced beam space via a linear transformation; and determines a weighting factor based on the mean likelihood. The factors are then normalized and utilized to correct and weigh the NDE images. Experiments on industrial samples of austenitic stainless steel and INCONEL Alloy 617 are conducted with a 5 MHz transducer array, and the results demonstrate that the grain noise is reduced by about 20 dB while the defect reflection is well retained, thus the great benefits of the method on enhanced defect detection and imaging in ultrasonic NDE are validated.

A robotic nondestructive inspection system developed for stainless steel dry storage canisters containing spent nuclear fuel was tested on a range of mockups in order to assess different aspects of the system. The nondestructive inspection was designed to be able to interrogate 100% of the canister weld lines, even if much of the surface is inaccessible because it uses ultrasonic shear-horizontal waves in what is basically a pulse-echo mode. The guided waves are sent and received by electromagnetic acoustic transducers, which are noncontact as well as tolerant of elevated temperature and gamma radiation. The nondestructive inspection targets stress corrosion cracks in the heat-affected zone of welds. The mockups enable determining the reflection and transmission ratios associated with the welds, the detectability of closed crack-like flaws, the detectability of branched cracks, B-scans along a weld line at elevated temperature, and full robotic system deployment. The test results demonstrate that the robotic system meets its functional requirements.

Model-assisted probability of detection (MAPOD) and sensitivity analysis (SA) are important for quantifying the inspection capability of nondestructive testing (NDT) systems. To improve the computational efficiency, this work proposes the use of polynomial chaos expansions (PCEs), integrated with least-angle regression (LARS), a basis-adaptive technique, and a hyperbolic truncation scheme, in lieu of the direct use of the physics-based measurement model in the MAPOD and SA calculations. The proposed method is demonstrated on three ultrasonic testing cases and compared with Monte Carlo sampling (MCS) of the physics model, MCS-based kriging, and the ordinary least-squares (OLS)-based PCE method. The results show that the probability of detection (POD) metrics of interests can be controlled within 1% accuracy relative to using the physics model directly. Comparison with metamodels shows that the LARS-based PCE method can provide up to an order of magnitude improvement in the computational efficiency.

Elastodynamic Green's function for anisotropic solids is required for wave propagation modeling in composites. Such modeling is needed for the interpretation of experimental results generated by ultrasonic excitation or mechanical vibration-based nondestructive evaluation tests of composite structures. For isotropic materials, the elastodynamic Green’s function can be obtained analytically. However, for anisotropic solids, numerical integration is required for the elastodynamic Green's function computation. It can be expressed as a summation of two integrals—a singular integral and a nonsingular (or regular) integral. The regular integral over the surface of a unit hemisphere needs to be evaluated numerically and is responsible for the majority of the computational time for the elastodynamic Green's function calculation. In this paper, it is shown that for transversely isotropic solids, which form a major portion of anisotropic materials, the integration domain of the regular part of the elastodynamic time-harmonic Green's function can be reduced from a hemisphere to a quarter-sphere. The analysis is performed in the frequency domain by considering time-harmonic Green's function. This improvement is then applied to a numerical example where it is shown that it nearly halves the computational time. This reduction in computational effort is important for a boundary element method and a distributed point source method whose computational efficiencies heavily depend on Green's function computational time.

A wireless nondestructive fault detection test for loose or damaged connectors is demonstrated. An architecture known as the conditioned multiclassification of stimulated emissions (CMSE) is pretrained on simulated and empirical radar outputs, and transfer learning is applied to classify connected and disconnected coaxial interconnections. The two main data conditioning methods of this architecture, a statistical signal analysis tool and a convolutional filter bank, are evaluated in order to determine the cost-value proposition of each component. Novel contributions of this technique include the use of two simulation-aided convolutional filter banks to generate a multinetwork ensemble and transfer learning from artificial neural networks trained on two primitive datasets revolving around the electromagnetic phenomena of reflection and filtering. A total of 560 different neural network topologies across four different signal conditioning configurations are considered, with all results compared against the current standard for measurement of cable and connection faults, time-domain reflectometry. Metrics used for comparison are time (training and evaluation), detection (connector engagement at state change detection), and clustering (projection space performance, used as a measure of transfer learning potential). It is determined that the full CMSE architecture performs best, with nearly any neural network topology of this configuration displaying an early detection improvement of 113% and requiring 30% less time to execute an individual classification versus the current standard, all while meeting the most stringent definitions of nondestructive evaluation (NDE).