Nondestructive Evaluation Measurements Research Articles

A machine learning surrogate model for time of flight diffraction measurements of rough defects

Understanding the uncertainty in nondestructive evaluation measurements of rough defects requires a stochastic analysis due to the random variation between the morphology of different defects. In previous studies, large numbers of finite element models of randomly generated rough defects have been run in order to gain an insight into the statistics of the uncertain results. This approach is limited due to the time taken to run each individual model (typically of the order of minutes per model) and so the total number of models run is limited. In this paper, a surrogate model approach is proposed which produces close approximations to an original finite element model of a time-of-flight diffraction measurement, but in a small fraction of the time (<1 ms). The surrogate model is trained on pairs of input (rough defect) and output (A-scan) data from the finite element model; the surrogate then learns the relationship between inputs and outputs and is then able to generate new results. The architecture employed is a machine learning sequence-to-sequence approach similar to those used in natural language algorithms, based on the idea of ‘translating’ between defect to A-scan. In this study, a surrogate model was trained on 2160 finite element results which spanned across a range of ultrasonic incidence angles, rough defect correlation length and root-mean-square roughness. The surrogate model is shown to produce A-scans with close agreement to those produced by the original finite element model; taking approximately 0.425 ms instead of approximately 8 min. Millions of results are then possible in seconds, rather than the decades that would be required for finite element studies; more complete stochastic analysis are therefore now viable.

Interaction effects from the elastodynamic scattering by two symmetric spherical cavities

The scattering of harmonic waves has been studied extensively for problems in quantum mechanics, acoustics, electromagnetics, and elasticity. Solutions to elastodynamic problems are the basis for ultrasonic non-destructive evaluation measurement models. Therefore, in this study, we investigate the use of the boundary element method (BEM) in the frequency domain using an off-boundary technique in which the observation points are taken inside the scattering object. This methodology removes both non-integrable singularities from the domain of integration along with avoiding ill-conditioning effects that occur at fictitious eigenfrequencies of which both require using special computationally demanding procedures to obtain solutions. Additionally, we employ both free and half-space fundamental solutions (Green’s displacement tensors) to investigate the elastodynamic scattering of an incident, plane, time-harmonic longitudinal wave in the frequency domain of a homogeneous, isotropic, and linear elastic solid with one or two spherical cavities. The half-space fundamental solution reduces the number of required boundary elements in half, which significantly reduces computational resource requirements. We only consider spherical cavities in this paper to illustrate the full and half-space off-boundary BEM and analyze the interaction effects associated with the elastodynamic scattering by two symmetric spherical cavities. To verify the validity of the free and half-space off-boundary BEM formulations, surface displacement results are compared with existing surface displacement results and show good agreement. Finally, the half-space off-boundary BEM is used to illustrate the interaction effects of the back and forward scattered displacement fields as a function of the distance from the spherical cavities.

The Influence of Creep Induced Grain Boundary Separation on Electrical Non-destructive Evaluation Measurements

The Influence of Creep Induced Grain Boundary Separation on Electrical Non-destructive Evaluation Measurements

Visually Encoded Contact Inspection System for EMATs

Contact inspections are commonly performed in industry to check for defects and degradation, such as corrosion or cracks. Non-destructive evaluation (NDE) probes are being deployed with increasing frequency using autonomous robots, especially in harsh environments or in areas where access is restricted and difficult. Together with the NDE measurement, it is important to capture the 3D position of the probe so that the location where the data originated is known. This allows for the generation of 3D maps of the inspection, which ensure full scan coverage, and can be used for inspection reports and to generate digital twins of the structure for asset management. In this paper, a full inspection system integrating a robot mountable stereo camera system together with an electromagnetic acoustic transducer (EMAT) probe and a wireless NDE data acquisition system is presented. The system is capable of capturing and merging 3D positional data of the probe as it is scanned and NDE data. The system design in terms of hardware and software is described in this paper. A set of tests to evaluate its performance on relevant structural components are also presented, and the results are reported and discussed.

Metal additively manufactured phononic materials as ultrasonic filters in nonlinear ultrasound measurements.

Nonlinear ultrasound (NLU) is a nondestructive evaluation method that is sensitive to damage at length scales well below those detected by conventional ultrasonic methods. Micro- and nano-scale damage correlates to the second harmonic generated by a sinusoidal wave as it propagates through a material. However, NLU measurements are plagued by experimentally-induced nonlinearities and require careful calibrations that have limited them to laboratory measurements. Here, we propose the use of additive manufacturing (AM) phononic materials with ultrasonic filtering properties to reduce extraneous nonlinearities. To do this, finite element simulations were first used to design and analyze phononic materials to transmit an ultrasonic wave but forbid the propagation of its second harmonic. Phononic filters were then fabricated with AM and experimentally characterized in the ultrasonic regime. Results show that the phononic materials behave as low-pass filters, where the cut-off frequency is controlled by the unit cell geometry and also influenced by defects and microstructure from the AM process. Finally, the phononic filters were incorporated into NLU measurements, demonstrating the removal of extraneous nonlinearities and thus better isolating second harmonic generation in a test sample. This work suggests that AM phononic materials could improve NLU and other nondestructive evaluation measurements.

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.

Novel Multifrequency Eddy Current NDT Signal Inversion Scheme for Aerostructures

Abstract Flaw profile characterization from nondestructive evaluation measurements is a typical nondestructive testing (NDT) inverse problem. Flaw profiling, particularly in aerospace industry, is important, as it is a decision tool to evaluate the air worthiness of the aerostructures. Accurate flaw profiling thus ensures aircraft safety through the timely implementation of cost-effective replacement/repair actions. Eddy current (EC) NDT data acquired at multiple frequencies contain complementary information about the flaws due to the skin effect phenomenon. However, finding the exact contribution of each measurement mode while determining the solution to the inverse problem is considerably challenging. In the reported research work, a novel multifrequency EC data-based polynomial model is formulated to solve the inverse problem. The weight (coefficient) of each measurement mode in the polynomial model is computed using nonlinear optimization techniques. Actual NDT data of a retired aircraft containing mutually exclusive training and test databases has been used as a case study. Data fusion (DF)-based inversion results are compared to the results acquired using individual measurement mode data. The improved results with DF indicate the efficacy of the proposed technique.

Confidence metric for signal classification in Non Destructive Evaluation

Quantitative assessment of the reliability of defect classification is critical in non-destructive evaluation (NDE) applications. Particularly in automated data analysis systems, such a measure enables the system to monitor its own performance and automatically flag indications where operator intervention is required. Apart from inherent ambiguity of non-discriminative features and inadequate training samples, noisy measurement is a primary reason underlying the classifier's unreliable decisions. In this paper, we have developed a framework to incorporate the major sources of classification errors into a single quantitative measure. By bootstrapping and weighting Bayes posterior probability with estimated noise distribution, effect of noise in NDE measurements is embedded in the resultant confidence measure. The effectiveness of the proposed method is first demonstrated on synthetic dataset from an eddy current simulation model. It is then used to analyze confidence of classifying experimental data from eddy current inspection of defects in steam generator tubes.

Development of an autonomous bridge deck inspection robotic system

AbstractThe threat to safety of aging bridges has been recognized as a critical concern to the general public due to the poor condition of many bridges in the United States. Currently, the bridge inspection is conducted manually, and it is not efficient to identify bridge condition deterioration in order to facilitate implementation of appropriate maintenance or rehabilitation procedures. In this paper, we report a new development of the autonomous mobile robotic system for bridge deck inspection and evaluation. The robot is integrated with several nondestructive evaluation (NDE) sensors and a navigation control algorithm to allow it to accurately and autonomously maneuver on the bridge deck to collect visual images and conduct NDE measurements. The developed robotic system can reduce the cost and time of the bridge deck data collection and inspection. For efficient bridge deck monitoring, the crack detection algorithm to build the deck crack map is presented in detail. The impact‐echo (IE), ultrasonic surface waves (USW), and electrical resistivity (ER) data collected by the robot are analyzed to generate the delamination, concrete elastic modulus, corrosion maps of the bridge deck, respectively. The presented robotic system has been successfully deployed to inspect numerous bridges in more than ten different states in the United States.

Statistical Performance Assessment of an NDE-Based SHM-DP Methodology for the Remaining Fatigue Life Prediction of Monitored Structural Components and Systems

Integrated structural health monitoring and damage prognosis (SHM-DP) methodologies, coupled with sensor-based nondestructive evaluation (NDE) techniques, are becoming increasingly important for the near-real-time condition assessment (i.e., SHM) and future performance predictions (i.e., DP) of aging mechanical systems, civil structures, and infrastructure networks, as well as automotive, naval, and aerospace vehicles. A successful SHM-DP strategy, capable of identifying all critical damage mechanisms while accounting for all relevant sources of uncertainty, can be used as an advanced tool to effectively and optimally manage the life-cycle of the monitored system, recursively forecast its remaining useful life (RUL), and ultimately reduce the overall ownership cost through dynamic reliability-based inspection and maintenance (RBIM) plans, system downtime minimization, catastrophic failure prevention, and potential RUL extension. In this perspective, fatigue damage propagation is one of the most critical and unpredictable deterioration processes for a large variety of structural and mechanical systems that are subjected repeatedly to cyclic and/or random operational loading during their service life. Within this limited scope, the authors developed a comprehensive NDE-based SHM-DP framework for recursively predicting the time-varying system reliability and the remaining fatigue life (RFL) of monitored systems subjected to deterioration by multi-site fatigue damage propagation. This paper provides a brief overview of the proposed framework and then uses a set of experimental fatigue test data to perform a thorough statistical performance assessment of the developed methodology at the local reliability component level (i.e., single damage mechanism and single damage location) including NDE detectability and measurement uncertainty as well as both load and model parameter uncertainty.

Data analysis and visualization for the bridge deck inspection and evaluation robotic system

Abstract Background Bridge deck inspection is essential task to monitor the health of the bridges. Condition monitoring and timely implementation of maintenance and rehabilitation procedures are needed to reduce future costs associated with bridge management. A number of Nondestructive Evaluation (NDE) technologies are currently used in bridge deck inspection and evaluation, including impact-echo (IE), ground penetrating radar (GPR), electrical resistivity (ER), ultrasonic surface waves (USW) testing, and visual inspection. However, current NDE data collection is manually conducted and thus faces with several problems such as prone to human errors, safety risks due to open traffic, and high cost process. Methods This paper reports the automated data collection and analysis for bridge decks based on our novel robotic system which can autonomously and accurately navigate on the bridge. The developed robotic system can lessen the cost and time of the bridge deck data collection and risks of human inspections. The advanced software is developed to allow the robot to collect visual images and conduct NDE measurements. The image stitching algorithm to build a whole bridge deck image from individual images is presented in detail. The ER, IE and USW data collected by the robot are analyzed to generate the corrosion, delamination and concrete elastic modulus maps of the deck, respectively. These condition maps provide detail information of the bridge deck quality. Conclusions The automated bridge deck data collection and analysis is developed. The image stitching algorithm allowed to generate a very high resolution image of the whole bridge deck, and the bridge viewer software allows to calibrate the stitched image to the bridge coordinate. The corrosion, delamination and elastic modulus maps were built based on ER, IE and USW data collected by the robot to provide easy evaluation and condition monitoring of bridge decks.

Quantitative Defect Detection inside Metal Casting Specimens by Means of MFES

An NDE measurement for internal defects of metal casting products is presented. The internal defects are often made in the manufacturing process of metal casting. From the view point of non-destructive evaluation (NDE), it is very difficult to make artificial defects that simulate actual internal defects. Therefore, we prepared a lot of metal casting samples that may include defects, and investigated the relation between the defect dimensions and the spectrogram that were generated by Multi-Frequency Excitation and Spectrogram Method (MFES).

Coded waveforms for optimised air-coupled ultrasonic nondestructive evaluation

This paper investigates various types of coded waveforms that could be used for air-coupled ultrasound, using a pulse compression approach to signal processing. These are needed because of the low signal-to-noise ratios that are found in many air-coupled ultrasonic nondestructive evaluation measurements, due to the large acoustic mismatch between air and many solid materials. The various waveforms, including both swept-frequency signals and those with binary modulation, are described, and their performance in the presence of noise is compared. It is shown that the optimum choice of modulation signal depends on the bandwidth available and the type of measurement being made.

Analytical Model for the Extraction of Flaw-Induced Current Interactions for SQUID NDE

Incorporating an analytical approach to simulate the interaction of a series of long cracks and the induced current of a double-D excitation coil, we have developed a model-based method to do precise detection of the positions of the cracks in a metallic structure by using eddy-current superconducting quantum interference device (SQUID) nondestructive evaluation (NDE) measurements. Conventionally, the structure of the defects is found by iteratively solving a numerical forward problem, which is usually based on finite-element, boundary-element, or volume-integral method. This, however, incurs a heavy numerical burden, as every time the forward problem is to be solved, a rigorous numerical model should be inevitably employed to extract the complex distribution pattern of the induced current encountering defects of the structure. In this paper, an analytical approach is used for the modeling of the interaction of the induced current and a series of cracks in the sample. It duly considers the distribution of the induced current in the flawed samples, does not call for extremely high computational resource, and thus permits efficient NDE as the forward problem can be solved within a reasonable time. Here, a high- <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tc</i> first-order radio-frequency SQUID gradiometer is employed as the magnetic sensor of the NDE system to scan the samples with different cracks. The accuracy of the proposed algorithm is verified by having the extracted shape of the defects obtained by applying the proposed algorithm on the SQUID NDE measurements against the actual cracks.

Giant Magnetoimpedance Sensor for Non Destructive Evaluation Eddy Current System

In this paper we present a simple and efficient Non Destructive Evaluation (NDE) eddy current system using GMI (Giant Magneto Impedance) magnetometer and single wire inducer. The performances of the system are determined and compared with a reference sensor. NDE measurements are done to detect corrosion defect on reference samples. A theoretical analysis with the finite element method compared to experimental measurements shows that the improved GMI (IGMI) magnetometer sensor could be easily used to detect defects like cracks or corrosions in agreement with sensor spatial resolution.

Eddy Current Technique Based on ${\rm HT}_{\rm c}$-SQUID and GMR Sensors for Non-Destructive Evaluation of Fiber/Metal Laminates

In this work we present non-destructive evaluation measurements on fiber/metal laminate specimen by using eddy current techniques employing HT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> SQUID (superconductive quantum interference device) and giant magneto-resistive (GMR) sensors. Our aim is to compare the performance and the capability of HT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> SQUID and GMR sensors to detect the presence of damage inside FML composite materials. Experimental results concerning the detection of artificial defects in aeronautical structures with high magnetic sensitivity by using HT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> SQUID, and with high spatial resolution using GMR, will be presented and discussed.

A Recursive Bayesian Estimation Method for Solving Electromagnetic Nondestructive Evaluation Inverse Problems

Estimating flaw profiles from measurements is a typical inverse problem in electromagnetic nondestructive evaluation (NDE). This paper proposes a novel state-space approach for solving such inverse problems. The approach is robust in the presence of measurement noise. It formulates the inverse problem as a tracking problem with state and measurement equations. The state-space model resembles the classical discrete-time tracking problem. The model allows recursive Bayesian nonlinear filters based on sequential Monte Carlo methods to be applied in conjunction with numerical models that represent the measurement process (i.e., solution of the forward problem). We apply our approach to simulated eddy-current and magnetic flux leakage NDE measurements (with and without measurement noise) from known flaw shapes, and the results indicate the feasibility and robustness of the proposed method.

Model‐based algorithms for detecting damage in ultrasonic nondestructive evaluation measurements

This work addresses the "As‐Built" modeling problem in ultrasonic nondestructive evaluation (NDE), in which one is given measurements of a mechanical part before and after use. The condition of the "as‐built" part (prior to use) is known, so that after the part has been used, it can be tested for damage. This enables a two‐step model‐based approach: (1) Given input‐output measurements (A‐Scans), estimate a dynamic prediction‐error model of the "as‐built" measurement(s) using system identification algorithms. The model is validated by testing the innovations (residuals) for statistical whiteness and then stored for future use. (2) Later, when testing the part for damage, the error between the measurement and the output of the stored model is tested against a short‐term whiteness confidence interval test statistic. If the part passes the test, this implies that the model remains valid and the part is declared undamaged. If the part fails the test, this indicates a model mismatch, which means that the part's acoustic properties have changed, and the part is declared damaged. Performance of the algorithms is demonstrated using real measurements, receiver operating characteristic (ROC) curves and a confidence interval about the probability of correct classification.

Correlation of residual fatigue life with quantified NDE measurements

Quantified metrics from nondestructive evaluation (NDE) data were used to predict residual fatigue life of corroded AA2024-T3 specimens. The specimens were corroded under a wide range of conditions to achieve a range of corrosion morphologies, including a mix of pitting, intergranular, and general corrosion. Ultrasonic testing (UT), white light interference microscopy (WLIM), and microradiography images were then analyzed to extract damage metrics. The corroded specimens were then fatigued to failure. The resulting data were used to obtain an excellent correlation between the NDE metrics and the residual fatigue life using multiple linear regression (MLR). While there are several important metrics that can be extracted from UT and WLIM, the correlation with microradiography metrics was weak in this damage range. Residual life models including WLIM surface roughness measurements performed better for lightly damaged specimens than those using only UT measurements. However, the addition of microradiography metrics resulted in no significant improvement to the correlation models.

Performance evaluation of NDE methods

Mathematical basis for the performance evaluation of Nondestructive Evaluation (NDE) methods is presented. In structural inspection with NDE methods, NDE data are not directly used for the condition assessment. Instead, NDE data must be interpreted as condition of inspected element. Correct assessments of conditions depend on many factors such as the inaccuracy and the variability in NDE measurements and the uncertainty in correlation between attributes (what is measured) and conditions (what is sought in the inspection). Inaccuracy and variability are the vulnerable nature of NDE methods, whereas uncertainty is the inevitable error introduced from attributes used by test device. Despite all the vulnerabilities using NDE methods, a reliable NDE method should provide correct assessment of condition, which may be the correct percent area damaged in an inspected element. To make the decision on the performance of NDE methods, this paper presents mathematical basis to measure the reliability of NDE methods.