Nondestructive Evaluation Methods Research Articles

Radio-Frequency Conductivity Evaluation Method Based on Surface/Interface Scattering of Metallic Coatings

Developing non-destructive evaluation methods for the radio frequency (RF) conductivity of conductive coatings can accelerate the performance evaluation and development of wireless communication devices. By using a split-resonator cavity to compare 800 nm copper/graphite and 1000 nm copper/graphite, we found that the RF conductivity increased by 45.5% and 82.7%, respectively, from 15 GHz to 40 GHz (pure copper was −7.2%), indicating that the bulk materials analysis method is not suitable for coating materials. Combined with electromagnetic wave theory, we believe that the critical factor lies in the additional losses of the electromagnetic waves at the copper/graphite interface and substrate. Based on the skin depth theory, the concept of triple skin depth is proposed to calculate the power loss of copper/graphite at different frequencies, considering rough Peff (including the power loss of the rough surface, copper coatings, copper/graphite interface, and graphite) compared with smooth pure copper Pc. Combined with the relationship between RF conductivity and electromagnetic wave power loss, the conductivity of copper coatings σCu at different frequencies is obtained by analyzing the measured σeff. Compared with the roughness model, the calculation error decreased from 30% to below 7%. Our study provides a theoretical basis for the regulation of the RF conductivity of metal coatings at different frequencies.

Inverse stimulation enables ultrasonic binary coding for NDE using a custom linear testing system

Ultrasonic testing is an established method of non-destructive evaluation. The increasing complexity of material systems requires an extension of conventional methods. In related fields such as radar and medical ultrasound, signal optimisation and coded stimulation are successfully used and offer great potential for optimising state-of-the-art measurements and extending applications. In our work, we highlight the difference between using a coded sequence to stimulate an ultrasonic testing system and the actual performance of the digital code to motivate the exploration of inverse stimulation. In order to study inverse stimulation, a custom-built ultrasonic system was designed. As a first step, the transfer function was obtained by testing pulse and chirp stimulation. In the next step, inverse stimulation was performed based on the linear transfer function to engineer the ultrasonic echoes to have shapes similar to the target code. Finally, the auto-correlation function of the ultrasonic echoes resulting from the inverse stimulation is compared with the function of the original code sequence and the agreement of the recorded ultrasonic echo with the spectrally limited code sequence. With this work we propose an integrated, low-voltage, fully linear ultrasonic testing system where the recording of a linear transfer function allows echo engineering even for a binary coded excitation sequence. We have demonstrated that inverse stimulation enables the generation of binary ultrasonic echoes with performance equal to the digital code.

Acoustic wave method for non-destructive evaluation of flattened bamboo veneers

This study examined the feasibility of acoustic stress wave velocity (V) evaluation commonly used to grade wood veneer for predicting the flexural Modulus of Elasticity (MOE) of flattened Moso bamboo. The correlation between MOE and V was low in bamboo compared with Douglas fir wood veneers, due to much lower grain angle deviation in bamboo. Bamboo had 30% lower MOE than Douglas-fir, likely linked to a combined effect of shorter cells and higher average microfibril angle of parenchyma cells in bamboo. Segmenting by density can effectively group flattened bamboo veneers into different MOE grades (R2 = 0.52) but assaying for both density and V gives more reliable prediction of apparent MOE (R2 = 0.86).

Microtexture region segmentation of eddy current testing data using a structural prior

Microtexture regions (MTRs) are collections of grains with similar crystallographic orientation. Because their presence in titanium alloys can significantly impact aerospace component life, a nondestructive method to detect and characterize MTR is needed. In this work, we propose to use data from two nondestructive evaluation methods, eddy current testing (ECT) and scanning acoustic microscopy (SAM), in order to recover the boundary and dominant crystallographic orientation of each MTR in a specimen. ECT is an electromagnetic method that is sensitive to changes in crystallographic orientation associated with MTR; however, its low resolution prevents it from resolving MTR boundaries well. In contrast, SAM is a high frequency ultrasound method that is able to resolve MTR boundaries but is not sensitive to orientation. This paper proposes an algorithm to characterize MTR that makes use of a method known as covariance generalized matching component analysis. This method is used to build a surrogate linear forward model that relates MTR boundaries and orientation to ECT data. The model is inverted using the SAM data as a structural prior. We demonstrate this technique using simulated ECT and experimental SAM data from a large grain titanium specimen.

Experimental and computational investigation into the damage mechanisms in composite metal hybrid panels subjected to high pressure blast loading

In this work, a free piston shock tube is utilized to conduct blast experiments on unidirectional glass fiber reinforced thermoplastic composite-metal hybrid panels. Damage and deflection characteristics are obtained in real time during the blast test using fringe projection methods, and post blast damage is assessed by nondestructive evaluation methods. Finite element models are developed in LS Dyna for the blast apparatus and hybrid composite panel and the results are compared to the experimental blast tests. The delamination in the computational model matched both size and shape to the delamination of the composite C-Scan. Furthermore, both dynamic deflection and permanent deflection of the hybrid panel correlated well between the experimental blast test and the LS Dyna blast simulation.

Lithium inventory tracking as a non-destructive battery evaluation and monitoring method

Lithium inventory tracking as a non-destructive battery evaluation and monitoring method

Quantifying predictive uncertainty in damage classification for nondestructive evaluation using Bayesian approximation and deep learning

Magnetic flux leakage (MFL), a widely used nondestructive evaluation (NDE) method, for inspecting pipelines to prevent potential long-term failures. However, during field testing, uncertainties can affect the accuracy of the inspection and the decision-making process regarding damage conditions. Therefore, it is essential to identify and quantify these uncertainties to ensure the reliability of the inspection. This study focuses on the uncertainties that arise during the inverse NDE process due to the dynamic magnetization process, which is affected by the relative motion of the MFL sensor and the material being tested. Specifically, the study investigates the uncertainties caused by sensing liftoff, which can affect the output signal of the sensing system. Due to the complexity of describing the forward uncertainty propagation process, this study compared two typical machine learning (ML)-based approximate Bayesian inference methods, convolutional neural network and deep ensemble, to address the input uncertainty from the MFL response data. Besides, an autoencoder method is applied to tackle the lack of experimental data for the training model by augmenting the dataset, which is constructed with the pre-trained model based on transfer learning. Prior knowledge learned from large simulated MFL signals can fine-tune the autoencoder model which enhances the subsequent learning process on experimental MFL data with faster generalization. The augmented data from the fine-tuned autoencoder is further applied for ML-based defect size classification. This study conducted prediction accuracy and uncertainty analysis with calibration, which can evaluate the prediction performance and reveal the relation between the liftoff uncertainty and prediction accuracy. Further, to strengthen the trustworthiness of the prediction results, the decision-making process guided by uncertainty is applied to provide valuable insights into the reliability of the final prediction results. Overall, the proposed framework for uncertainty quantification offers valuable insights into the assessment of reliability in MFL-based decision-making and inverse problems.

Nondestructive material characterization and component identification in sheet metal processing with electromagnetic methods

Electromagnetic methods for non-destructive evaluation (NDE) are presented, with which sheet metal components can be identified and their material properties can be characterized. The latter is possible with 3MA, the Micromagnetic Multiparametric Microstructure and stress Analyser. This is a combination of several micromagnetic NDE methods that make it possible to analyse the microstructure in a ferromagnetic material and to determine quantitative values of the mechanical material properties or the stress state. In the case of cold forming, the 3MA application for pre-process testing of sheet metal is discussed. Based on the 3MA information, the formability of the sheets can be predicted. To apply 3MA in-line, the influence of the relative speed and the relative distance between the 3MA probe head and the sheet was investigated. In a second study, a spatially resolved eddy current (EC) method was used to create an image of the intrinsic material microstructure of a component for its identification and traceability. It turned out, that these intrinsic fingerprint images can still be recognized even after subsequent plastic deformation or coating of the surface. This enabled the development of a marker-free traceability method for sheet metal processing. It is based on a low-cost array sensor and a specimen identification using robust and partly redundant features of the fingerprint images processed by machine learning (ML).

Higher-order thin layer method as an efficient forward model for calculating dispersion curves of surface and Lamb waves in layered media

The thin layer method (TLM) is an effective semi-discrete numerical tool for analyzing wave motion in stratified media. The TLM can calculate the eigenvalues and eigenvectors for both propagating and decaying waves, which is essential to simulate waves near the source. The eigenvectors are particularly useful in various applications, such as estimating modal contributions, calculating synthetic seismograms, determining Green’s function, analyzing site response, and solving wave amplification problems in earthquake engineering, to name a few. In practice, most engineering applications use linear element-based TLM. However, the overall performance of TLM depends upon the type of element and the number of thin layers. The linear element TLM requires relatively thin sub-layers to obtain an accurate solution, making it computationally expensive and time-consuming. This study explores the potential of using non-linear higher-order TLM (HTLM) for forward modeling. The mathematical framework of TLM is used to formulate the generalized HTLM in determining the multimodal dispersion curve of Rayleigh, Love, and Lamb waves. The convergence and computational efficiency of different orders of HTLM have been investigated on a diverse set of published soil profiles and plate-like structures. Since non-linear interpolation functions better capture the change in displacement and stress between the layers, the findings reveal that the HTLM consistently outperforms the linear element TLM in terms of accuracy. Furthermore, the computational efficiency of different order HTLM is analyzed by comparing the total degrees of freedom and CPU time. Remarkably, higher-order HTLMs exhibit improved accuracy without increasing the computational burden, making them an attractive option for practical applications such as global search-based inversion and full spectrum inversions where fast and economical forward modeling is essential. Therefore, the HTLM will become helpful in seismic wavefield modeling, dynamic soil characterization, non-destructive evaluation methods, wave propagation analysis through waveguides, seismic site response analysis, etc.

Dynamic Defect Detection in Fast, Robust NDE Methods by Transfer Learning Based Optimally Binned Hypothesis Tests

ABSTRACT The structural integrity of safety-critical infrastructures diminishes over time, necessitating periodic inspections. The gathering of low-noise, precise nondestructive evaluation (NDE) readings for defect detection can be both time-consuming and costly. In practical applications, NDE data can be highly noisy due to factors like liftoff/stand-off distances, probe drift, scanning speed, and variations in data acquisition rates. Given the broad spectrum of potential uncertainties that can contribute to noise accumulation, characterizing the noise in such NDE data presents significant difficulties. As a result, the extraction of defect signals through deconvolution becomes challenging. This article introduces an NDE-based dynamic defect tracking framework designed for robust conditions. It incorporates regular periodic monitoring of the material under test (MUT) using a cost-effective, easy-to-implement magnetic flux leakage (MFL) probe. The framework proposes a dynamic setup that includes occasional, highly accurate, expensive MFL scans among regular, low-cost, noisy MFL scans. The objective is to detect and precisely track defect formation in metallic pipes over time, thereby enabling the efficient alarming and localization of defects before they reach detrimental sizes. A key characteristic of the proposed dynamic monitoring method is its noise type agnosticism in the low-cost MFL scans. It remains effective even when the underlying noise-producing uncertainties fluctuate significantly over time, a feature achieved through the use of transfer learning. The method transfers accurate information regarding the locations and sizes of existing corrosions from the detailed MFL scan to the subsequent noisy MFL inspections. With this supportive information from defective scan-points, it estimates the degradation of the signal-to-noise ratio (SNR) in the noisy MFL scans. The framework suggests binned hypothesis tests in noisy MFL scans, setting the level of aggregation based on the SNR estimate in the scan data. This proposed binned hypothesis test optimizes defect coverage and maintains control over the false discovery rate (FDR). Experimental MFL data, gathered under a wide range of uncertainties and defect types, illustrates the highly heterogeneous nature of noise distributions in low-cost MFL scans and the significant variance in SNR deteriorations. The necessity of using binned hypothesis tests in these low-cost MFL data is highlighted by the poor performance of pointwise multiple hypothesis tests. Finally, the high effectiveness of the proposed transfer learning-based binned hypothesis testing method is demonstrated. Although the method was applied to MFL data in this instance, it is also suitable for other NDE applications. The applicability of the algorithms has also been confirmed through validation on experimentally generated Eddy Current (EC) data.

Experimental studies on fillet weldment joints by artisans in Ghana’s Western Region

Constant progress, demand in the manufacturing of mechanisms parts, and the ability to join parts together is another critical aspect of manufacturing for assemblers. Joining methods plays a significant role and welding must be constantly improved, experimented with, and upgraded due to its increasing use in all aspects of manufacturing. The quality of a weld joint is highly dependent on the process parameter. Welds are examined to ensure that they meet specifications through various experimental processes ranging from computational networks, evolutionary algorithms, non-destructive testing (NDT) and destructive testing (DE) to ascertain the quality of joints. The main objective of the study was to ascertain the quality of welded joints through non-destructive evaluation (NDE). To achieve this goal, an experimental method of four (4) NDE methods were employed to evaluate the quality of weldment joints on a mild steel material joined using manual metal arc welding (MMAW) process in selected fabrication shops in Takoradi-Kokompe, Ghana. Steel specimens were prepared and taken to the artisans to be created into corner joints using the fillet method. The specimens were tested using non-destructive testing techniques. The results on the welded joints were evaluated using the AWS D1.1 code of acceptance for structural steels. The results revealed that all welded joints from fabricators failed to meet the AWS D1.1:2000 acceptance criteria and, thus, had to be rejected. The results implies that through the artisans have worked for many years in the welding trade, their lack of competence and skills in the selection of right input welding parameters contributed to the results. The results implies that, the finished artefacts may look fine but internal structures may contribute to future failure of the fabricated artefacts.

Evaluation of the Embrittlement in Reactor Pressure-Vessel Steels Using a Hybrid Nondestructive Electromagnetic Testing and Evaluation Approach.

The nondestructive determination of the neutron-irradiation-induced embrittlement of nuclear reactor pressure-vessel steel is a very important and recent problem. Within the scope of the so-called NOMAD project funded by the Euratom research and training program, novel nondestructive electromagnetic testing and evaluation (NDE) methods were applied to the inspection of irradiated reactor pressure-vessel steel. In this review, the most important results of this project are summarized. Different methods were used and compared with each other. The measurement results were compared with the destructively determined ductile-to-brittle transition temperature (DBTT) values. Three magnetic methods, 3MA (micromagnetic, multiparameter, microstructure and stress analysis), MAT (magnetic adaptive testing), and Barkhausen noise technique (MBN), were found to be the most promising techniques. The results of these methods were in good agreement with each other. A good correlation was found between the magnetic parameters and the DBTT values. The basic idea of the NOMAD project is to use a multi-method/multi-parameter approach and to focus on the synergies that allow us to recognize the side effects, therefore suppressing them at the same time. Different types of machine-learning (ML) algorithms were tested in a competitive manner, and their performances were evaluated. The important outcome of the ML technique is that not only one but several different ML techniques could reach the required precision and reliability, i.e., keeping the DBTT prediction error lower than a ±25 °C threshold, which was previously not possible for any of the NDE methods as single entities. A calibration/training procedure was carried out on the merged outcome of the testing methods with excellent results to predict the transition temperature, yield strength, and mechanical hardness for all investigated materials. Our results, achieved within the NOMAD project, can be useful for the future potential introduction of this (and, in general, any) nondestructive evolution method.

Non-Destructive Evaluation of Material Stiffness beneath Pile Foundations Tip Using Harmonic Wavelet Transform

Pile foundations are used to support superstructures and play an important role in the safety of these structures. The performance of pile foundations generally depends on the conditions of the pile itself and the material under the pile tip(i.e., bottom), especially for end-bearing piles installed in soft soil volumes. Therefore, to assess the performance of existing pile foundations, it is crucial not only to evaluate the structural integrity of the pile itself, but also to assess the ground conditions, such as subsoil stiffness beneath the pile foundation tip. Accessing the subsoil beneath the pile foundation tip is highly challenging in the field. Hence, there is a need for the development of non-destructive pile evaluation methods that allow the assessment of subsoil stiffness beneath the pile tip without direct access to the subsoil. Various non-destructive methods have been developed for pile performance assessment. However, these conventional non-destructive methods are primarily designed for assessing the structural integrity of the pile itself, and there are no existing non-destructive pile integrity testing methods applicable to evaluate the subsoil stiffness beneath the pile tip. In this study, a non-destructive method is developed to evaluate the subsurface soil stiffness beneath pile tip without direct access. The proposed method involves applying impact loading to the easily accessible pile head and measuring the elastic waves propagated within the pile foundation due to the impact loading. These wave signals are then recorded at the pile head. The measured time–history signals are decomposed using harmonic wavelet transform. This allows the obtainment of well-defined magnitude and phase information over time for various individual frequency components composing the wave. In this study, a method is proposed to assess the stiffness of the subsoil beneath the pile tip by simultaneously utilizing the magnitude and phase information of the measured signals obtained through harmonic wavelet transform. To facilitate this, a step-by-step data analysis procedure for evaluating the subsoil stiffness beneath the pile tip is introduced. To validate the proposed method, numerical simulations were conducted using ABAQUS. The experimental data obtained from the numerical simulations were processed using the proposed method to assess the subsoil stiffness beneath the pile. The determined subsoil stiffness was then compared with the exact soil stiffness used in the numerical simulation to evaluate the validity of the proposed method. Through this analysis, the proposed method demonstrated its effectiveness in assessing the subsoil stiffness beneath piles tip installed in weak soil volume.

Induced Current Thermo-Electrical Impedance Tomography for Nonferromagnetic Metal Material Surface Defect Profile Reconstruction

Nonferromagnetic metal materials are widely used in industry. Defects generated during manufacture and use may lead to serious accidents. The defect reconstruction is important for nondestructive evaluation. Eddy current pulsed thermography (ECPT) is a well-known nondestructive testing and evaluation method, but hardly reconstruct fully defect profile. Electrical impedance tomography (EIT) shows promising potential in defect profile reconstruction, but suffers from electrode number limitation. In this paper, EIT is introduced to ECPT image sequences processing, and a new method, induced current thermo-electrical impedance tomography (ICTEIT), is developed. The proposed method takes each infrared camera pixel as a virtual electrode, captures the electric current distribution with spatial resolution as high as camera, and reconstructs conductivity distribution at pixel level from which the defect profile can be identified. Experiments with different defects are carried out to verify the performance of the proposed method.

Real Time Defect Identification using Advanced Artificial Intelligence based Material Strain Sensors for Environmental Safety

Advanced materials find applications in various fields of engineering and medicine. Many advanced materials are synthesized characterised to design sensors for various application including environmental protection. This work explores the possibility of real time defect identification using advanced materials such as graphene composite based sensor for Environment safety. Advanced materials like Graphene composite based sensor were prepared using solvent casting technique. Possibility of using graphene-PVDF based sensors as non- destructive probes to identify surface defects of materials were evaluated by probing on materials with pre-machined defects. Defects were induced artificially in different materials such as (SS316 LN stainless steel) flat plate and cylindrical disc plate. Defects vary both in size and depth. Nanocomposite films were cut in to small pieces of equal dimension and pasted over the various defects. The variation in size and depth of the defects is measured in terms of variation in resistance and suitably calibration graphs were obtained. Using advanced materials for environmental protection, Industry environment safety can be monitored and essential measures can be carried out. This work also explores the change in absorption property of carbon based nanocomposite films on addition of different weight percentage of advanced material based fillers such as Multiwalled carbon nanotubes and graphene. Filler weight percentage increases in matrix which is indicated by red shift in the absorption spectra.In this research work the use of advanced materials for environmental protection in the Metallurgical Industry is explored . Development of advanced functional materials were carried out . Characterization of these advanced material were also carried out . Sensitivity of these advanced material based sensors were analyzed and calibarated. By using these advanced material based senors catastrophic disorders can be avoided and environmental safety is ensured.The non-destructive evaluation (NDE) method that is being presented uses a Carbon Nanotube (CNT) and graphene-based sensor to identify defects in pre-machined stainless steel plates is innovative and straightforward. Utilizing nano composites is meant to combine the advantageous qualities of the filler and matrix. It was decided to use PVDF, a piezoelectric polymer, because of its benefits including low cost, versatility, and quick response time. A small amount (weight%) of MWCNTs and graphene are employed as filler to improve the material's shortcomings, such as delayed electrical response, and to make it behave as a piezoresistive and conductive material.

An Electrical Resistance Diagnostic for Conductivity Monitoring in Laser Powder Bed Fusion.

With the growing interest in metal additive manufacturing using laser powder bed fusion (LPBF), there is a need for advanced in-situ nondestructive evaluation (NDE) methods that can dynamically monitor manufacturing process-related variations, that can be used as a feedback mechanism to further improve the manufacturing process, leading to parts with improved microstructural properties and mechanical properties. Current NDE techniques either lack sensitivity beyond build layer, are costly or time-consuming, or are not compatible for in-situ integration. In this research, we develop an electrical resistance diagnostic for in-situ monitoring of powder fused regions during laser powder bed fusion printing. The technique relies on injecting current into the build plate and detecting voltage differences from conductive variations during printing using a simple, cheap four-point electrode array directly connected to the build plate. A computational model will be utilized to determine sensitivities of the approach, and preliminary experiments will be performed during the printing process to test the overall approach.

Application of FTIR Spectroscopy method for The Quantification of Ascorbic Acid in Bulk Materials and Pharmaceutical Formulation

The present study illustrates the potential using of Fourier Transform Infrared Spectroscopy (FT-IR) in pharmaceutical analysis. A simple, rapid, non-destructive, and green (FT-IR) spectroscopy method for quality control evaluation of Ascorbic acid was developed, using potassium bromide (KBr) as a matrix to quantify the drug in bulk pharmaceutical materials and AUC its dosage forms. The sample preparation was done by mixing and grinding for disk composition and without using organic solvents. Absorbance obtained for the carbon-carbon band (C=C) located at the range (1600-1700) cm-1 was used to develop a calibration curve based on the AUC measurements. Method had been validated according to the International Conference on Harmonization (ICH) guidelines. Linearity, accuracy, precision, robustness, LOD and LOQ were evaluated and showed acceptable results for method validation in the concentration range of (2 - 12.5 mg). The validated method was able to apply for the quality control analysis of Ascorbic Acid raw materials and pharmaceutical dosage forms.

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).

Continuous laser-line scanning thermography with data-processing algorithm for rapid and accurate defect inspection

As an emerging nondestructive evaluation method, line-scanning thermography is efficient for defect detection and location. In this study, continuous laser-line scanning thermography combined with a corresponding data-processing algorithm is developed to realize highly accurate and highly efficient nondestructive evaluation of large structures. First, the configuration of continuous laser-line-scanning thermography is investigated, and a data-processing algorithm is established using the visual reconstruction method and the distortion correction of captured thermal sequence images. Subsequently, experiments are conducted on samples with defects at different scanning speeds to verify the stability and accuracy of the proposed method. The results indicate that the continuous laser-line scanning thermography is robust for defect detection at different scanning speeds. Highly accurate measurements of defects can be obtained under information redundancy or absence when the sampling frequency does not exactly match the scanning speed. Quantitative experimental comparison shows that the relative error of the defect measurement reduces significantly after the distortion of the initial reconstructed thermal image is corrected. Finally, the effects of the scanning velocity and selected pixel line on the defect detectability are investigated for image reconstruction. The proposed approach is shown to be promising for the continuous nondestructive evaluation of large structures in terms of accuracy and efficiency.

Evaluation of steel corrosion-induced concrete damage using electrical resistivity measurements

The main objective of this research study is to demonstrate the feasibility of Electrical Resistivity (ER) measurements as an in-situ, non-destructive evaluation method for monitoring progressive damage in concrete due to steel corrosion, particularly in a marine environment. To this end, a series of laboratory experiments is performed to investigate the variation of ER values in a total of 108 reinforced concrete cubes, each with a side dimension of 200 mm. The impressed current technique is utilized to accelerate the corrosion of steel reinforcement in the concrete cubes submerged in 3.0% NaCl solution, which simulates various degrees of concrete degradation. ER measurements are performed before and after the accelerated corrosion tests on the surface of concrete specimens. It is demonstrated that ER is an excellent NDE method for the monitoring of progressive concrete damage associated with chloride-induced steel corrosion.