Non-destructive Testing Methods Research Articles

Analytical Approach of three-dimensional Flawed Multilayered Structure for EC Inspection

This article is devoted to the development of two current source models for the analysis of 3D electromagnetic phenomena in a conductive part of spherical geometry based on the second-order magnetic vector potential method for eddy current nondestructive testing (ECNDT). The first model, for a homogeneous single-layer sphere, allows the analytical calculation of all electromagnetic quantities in the charge. The second allows to treat in addition to the three-dimensional aspect of the problem, the heterogeneity of the charge (with several layers of different conductivities). An investigations using a multifrequency system have shown better results in detection of small cracks. The observations take about the calculation of normalized impedance based on dissipate energy and Joule losses in conductive material.

Defect classification of composite materials using transfer learning methods

ABSTRACT Nowadays, composite materials have become prevalent across various sectors, particularly finding usage in large-scale applications such as spaceships, automobiles, and aircrafts. The accurate detection of the defects in these materials is crucial, yet traditional methods often rely on human inspection, which is susceptible to errors. Recent advancements in machine learning have enabled defect detection using ultrasonic non-destructive testing methods. This paper introduces a new dataset named UNDT, which is obtained from the scans of 60 different composite materials, generating a total of 1150 images depicting both defective and non-defective areas. Several transfer learning methods are applied on the newly introduced UNDT dataset as well as the publicly available USimgAIST ultrasonic dataset. Comparative performance assessments illustrate the significance of utilising the transfer learning approach for defect classification on ultrasonic inspection images. Furthermore, the research emphasises the substantial benefits of employing these transfer learning methods. Notably, the DenseNet121 and VGG19 models achieve the highest accuracy rates, with 98.8% and 98.6% on the UNDT and USimgAIST datasets, respectively.

Study on the Influence of Density and Water–Cement Ratio on the Cement Utilization, Fluidity, Mechanical Properties, and Water Absorption of Foam Concrete

In this paper, we study the influence of density and the water–cement (W/C) ratio on the slurry fluidity, compressive strength, and water absorption of foamed concrete (FC) and its mechanism of action, with the aim of proposing an optimal mix ratio for FC to reduce cement usage and carbon emissions in the construction industry and ensure the good overall performance of FC. In this experiment, two groups of experiments were designed using the control variable method. Fluidity and uniaxial compression tests showed that when the density was 600 kg/m3 and the W/C ratio was 0.6, the FC slurry had maximum fluidity, but its mechanical properties were poor and it collapsed easily. Conversely, by analyzing the uniaxial compressive strength/cement (UCS/C) ratio, it was observed that the mix ratio had a maximum cement utilization rate (W/C ratio) of 0.5 and a density of 1000 kg/m3. Nondestructive testing methods were used to measure the ultrasonic pulse velocity (UPV) and rebound value of the FC test block, and the strength and durability of FC were analyzed. The water absorption rate of the FC test block was tested, and the final analysis showed that the optimal mix ratio of FC in this test was W/C = 0.5, with a density of 1000 kg/m3.

Application of Artificial Intelligence Technology for Prompt Diagnosis of Cast Iron Mechanical Properties

Kinetic indentation is widely used to measure physical and mechanical properties of materials as one of the most universal methods for non-destructive testing. This paper uses the latest advances in artificial intelligence and capabilities of the Python programming language libraries allowing to carry out accurate measurements of cast iron hardness based on the data of the material’s micro-impact loading diagram. It has been shown that use of machine learning allows eliminating gross errors and reducing the error of indirect hardness evaluation in several times – down to 10 units according to Brinell HB. It has also been established that formation of additional features for training models (based on traditionally used characteristics: penetration depths, indenter movement speed and contact forces at certain points in time) has a positive effect on the accuracy of measurements, but amount of measurements should also be optimized. Feasibility of effective use of machine learning to evaluate hardness has been demonstrated by comparing of calculated hardness values with data obtained with standard testing methods. Advantage of the developed testing method is the fact that the developed algorithms can be used for prompt diagnostics of cast iron hardness using existing equipment. It is appropriate to extend the proposed approach for determination of other mechanical properties of cast iron: yield strength, strain hardening index, creep, relaxation, determined by indentation methods.

Evaluation of Quality for Flow Drill Screw (FDS) Joining Process Based on Loosening Torque

Although the SPR process is widely used for joining of multi-materials, the FDS process is used when one-way joining is required, such as space constraints or closed cross-sectional parts. The quality inspection for the FDS process is basically an appearance inspection or cross-sectional inspection. There is no non-destructive test method that can be used in the actual mass production line, and a method of utilizing loosening torque is being studied. In this study, the factors affecting the loosening torque are analyzed, and an experimental equation that can predict the loosening torque is derived. Since friction force between the screw and the upper plate and sticking on the screw thread resist against loosening torque, the prediction equation for each factor is considered and the least square method is used to obtain the coefficients of the experimental equation. The prediction equation of the loosening torque derived through experiments on 36 material combinations predicts the experimental values well. When a quality requirement value with a given margin is applied, it can be used as a non-destructive test method.

Fatigue performance of repair-welded and HFMI-treated transverse stiffeners

AbstractLarge portions of infrastructure buildings, for example, highway and railway bridges, are steel constructions and reach the end of their service life due to an increase of traffic volume. Repair welding can restore the current welded constructional detail with a similar fatigue strength. However, due to the increase of fatigue loading (traffic), an increase of fatigue strength is needed in such bridge structures. For this reason, the combination of repair welding and high-frequency mechanical impact (HFMI) treatment was investigated in this study in order to quantify the increase of fatigue life by combining both methods. For this, transverse stiffeners made of steel grade S355J2 + N were subjected to fatigue loading until a pre-determined crack depth was reached. The cracks were detected by non-destructive testing methods. Weld repair was realized by removing the material containing the crack and re-welded by a gas metal arc welding (GMAW) process, following that post weld treated was applied by HFMI-treatment and the specimens were subjected to fatigue loading again. Hardness profiles, weld geometries, and residual stress states were investigated for both the original and the repaired condition. In the repaired condition without additional HFMI treatment, a similar fatigue life than in the original condition is observed for the specimens. The repair-welded and HFMI-treated specimens reach a significant higher fatigue life compared to the repaired ones in the as-welded condition.

Time‐domain spectra of ultrasonic wave transmitted through granite and gypsum samples containing artificial defects

AbstractThe internal defects in rock masses can significantly impact the quality and safety of geotechnical projects. Mechanical waves, as a common nondestructive testing (NDT) method, can reflect the external and internal structures of rock or rock masses. Analyses on the reflected and transmitted waves enable nondestructive identification and assessment of potential defects within rocks. Previous studies mainly focused on the variation of single or limited wave features like main frequency, amplitude and energy between the intact and non‐intact samples. In fact, most information contained in the waveforms is neglected. Techniques of data mining can provide a powerful tool to reveal this information and therefore a more accurate determination of the internal structures. In this study, 995,412 NDT data from 14 types of granite and gypsum samples with different cross‐section shapes and different types of defects are recorded by an ultrasonic wave generation and collection system. This dataset can be used not only as the training data for defect classification in NDT but also as a good reference for conventional NDT analyses. Besides, time‐series data analysis is an opportunity and challenging issue, this dataset holds great potential for broader application in general time‐series classification analysis.

Fatigue cracking prediction of cobblestone interlayer pavement using non-destructive testing and mechanistic-empirical analyses

Several non-destructive testing (NTD) methods have been used to measure surface deflection, which makes to determine the elastic moduli of pavement layers through the back-calculation process and assess the structural capacity of asphalt pavements. In this study was evaluated the back-calculated moduli of the cobblestone interlayer pavements and the load capacity of this type of pavement related to the fatigue cracking criterion based on a mechanistic-empirical analysis. The employed methodology included the performance of on-site trials using non-destructive testing with the Falling Weight Deflectometer (FWD) devices on 84 test points in granular and cobblestone interlayer pavements, determination of deflection basin parameters (DBP), back-calculation layers’ moduli, and estimate of the fatigue cracking performance of the pavements by mechanistic-empirical analyses in MeDiNa software. The pavements with a cobblestone base layer displayed greater deflection measurements on the load application point compared to those measured on pavements with a granular base layer, indicating that conventional pavement displayed more stiffness. Cobblestone interlayer pavement displayed greater amounts of cracked area compared to granular base layer pavements showing lower load capacity based on the fatigue criterion. The DBP-based method by FWD test was able to identify the structural differences between the layers of pavements evaluated and identify the cracking evolution.

Noise robust damage detection of laminated composites using multichannel wavelet-enhanced deep learning model

This paper presents a noise-robust damage detection framework for composite structures via a commonly used vibration-based non-destructive testing (NDT) method. Recently, deep learning-based models have shown promising performance in the autonomous damage detection of laminated composites; however, the poor noise robustness of these models has plagued data-driven damage detection. Moreover, none of the existing studies on damage detection in laminated composites focus on noise-robust deep learning models with high generalization ability. Therefore, this study proposes a hybrid deep learning framework called a multi-channel convolutional autoencoder-support vector machine (MC-CAE-SVM) based on empirical mode decomposition (EMD) and correlation analysis for noise-robust damage detection. This framework aims to first decompose the vibrational signal from multiple health states into intrinsic mode functions (IMFs). Secondly, highly correlated IMFs were extracted using correlation analysis to remove noisy IMFs. Finally, these IMFs were transformed into a time-frequency representation using continuous wavelet transform (CWT) and input to the MC-CAE-SVM model for feature learning and damage detection. Additionally, the accuracy and sensitivity of the model to damage are enhanced by optimizing the MC-CAE-SVM model hyperparameters. Moreover, anti-noise analysis is performed to check the noise-robustness of the proposed model by incorporating noise at various levels. The results showed that the proposed model can provide better damage detection performance compared to conventional models with excellent noise robustness.

In-situ detection of electrolyte defects in lithium-ion batteries using ultrasonic imaging and analysis of formation patterns

The urgent necessity for enhanced cycling performance in lithium batteries is paramount for electric vehicles to attain broader economic advantages. However, prolonged usage and cycling often lead to electrolyte de-wetting within the battery, posing a significant risk to battery safety and lifespan reduction. A thorough understanding of the mechanisms underlying electrolyte de-wetting is pivotal for the development of long-lasting batteries. This study employs an ultrasonic non-destructive testing method to investigate changes in the internal electrolyte during battery operation. Analysis of the results found that during to the relatively higher current density and temperature near the tabs, electrolyte de-wetting is more prone to occur in these areas, with defects being more severe at the positive electrode compared to the negative electrode. Finally, these issues are addressed to provide reasonable recommendations for future battery design and optimization.

Detecting Multi-Scale Defects in Material Extrusion Additive Manufacturing of Fiber-Reinforced Thermoplastic Composites: A Review of Challenges and Advanced Non-Destructive Testing Techniques

Additive manufacturing (AM) defects present significant challenges in fiber-reinforced thermoplastic composites (FRTPCs), directly impacting both their structural and non-structural performance. In structures produced through material extrusion-based AM, specifically fused filament fabrication (FFF), the layer-by-layer deposition can introduce defects such as porosity (up to 10–15% in some cases), delamination, voids, fiber misalignment, and incomplete fusion between layers. These defects compromise mechanical properties, leading to reduction of up to 30% in tensile strength and, in some cases, up to 20% in fatigue life, severely diminishing the composite’s overall performance and structural integrity. Conventional non-destructive testing (NDT) techniques often struggle to detect such multi-scale defects efficiently, especially when resolution, penetration depth, or material heterogeneity pose challenges. This review critically examines manufacturing defects in FRTPCs, classifying FFF-induced defects based on morphology, location, and size. Advanced NDT techniques, such as micro-computed tomography (micro-CT), which is capable of detecting voids smaller than 10 µm, and structural health monitoring (SHM) systems integrated with self-sensing fibers, are discussed. The role of machine-learning (ML) algorithms in enhancing the sensitivity and reliability of NDT methods is also highlighted, showing that ML integration can improve defect detection by up to 25–30% compared to traditional NDT techniques. Finally, the potential of self-reporting FRTPCs, equipped with continuous fibers for real-time defect detection and in situ SHM, is investigated. By integrating ML-enhanced NDT with self-reporting FRTPCs, the accuracy and efficiency of defect detection can be significantly improved, fostering broader adoption of AM in aerospace applications by enabling the production of more reliable, defect-minimized FRTPC components.

Reliability and partial factor‐based assessment of a highway bridge supported by nondestructive testing

AbstractThe preservation of the degrading transport infrastructure is vital, as replacing it is impossible in view of limited budgets and environmental impacts. One component in overcoming this challenge is the evaluation of the reliability and the calculation of the remaining service life of existing structures. These structural reliability reassessments allow for the identification and utilization of load‐bearing capacity reserves. The consideration of actual structural characteristics and environmental conditions, various of which can be gathered by means of numerous measurement and inspection techniques, has the potential for more realistic computation results and therefore more economic decisions about the operation as well as maintenance activities. This article attempts to shed light on the potential of nondestructive testing (NDT) methods for updating the input variables in the ultimate limit states during a recalculation. The approach of the NDT‐supported reliability reassessment is demonstrated using a prestressed concrete bridge emphasizing the bending proof in transversal direction. As a result, the semi‐probabilistic, probabilistic and NDT‐based reassessment results are compared and the effects of NDT‐supported structural analysis on the calculated reliability are highlighted.

Comparison of Immersion and Portable Ultrasonic Housing to Quantify the Adhesive Bond Thickness and Sizing of Foreign Objects.

High-performance materials, such as carbon fiber laminates, are costly to manufacture and are often used in demanding environments requiring the use of high-resolution non-destructive testing (NDT) methods to confirm the integrity of the parts. One NDT method that has shown promise for qualifying carbon fiber laminates is the use of immersion ultrasound with spherically focused probes. However, many parts may not be submersible in an immersion tank due to size or material constraints. These parts must be scanned with contact transducers with inferior resolutions or with expensive and messy systems such as bubblers. This research presents the use of a novel housing system that allows for the use of focused immersion transducers in an out-of-tank portable ultrasonic scanning application. This work presents a comparison between scans taken using a custom high-resolution immersion system and scans taken using the presented housing. There are a wide variety of potential inspection applications for this novel system, and the present work focused on two specific applications: the quantification of the spatially varying adhesive thickness in bonded carbon fiber laminates and the quantification of foreign object inclusions in carbon fiber laminates. The results presented show that scans using the portable housing are comparable in quality to scans performed using an immersion system. Specifically, both inspection approaches had an average error of 0.04 mm when quantifying the adhesive thickness of a bonded composite, and for the foreign object detection, the error in quantifying the dimensions of the embedded foreign object was 0.1 mm and 0.2 mm for the immersion system and the portable inspection system, respectively. The demonstration was performed in a laboratory setting, but a discussion is provided for the necessary improvements needed to extend the system for use in field applications.

Towards Advancing Real-Time Railroad Inspection Using a Directional Eddy Current Probe

In the field of railroad safety, the effective detection of surface cracks is critical, necessitating reliable, high-speed, non-destructive testing (NDT) methods. This study introduces a hybrid Eddy Current Testing (ECT) probe, specifically engineered for railroad inspection, to address the common issue of “lift-off noise” due to varying distances between the probe and the test material. Unlike traditional ECT methods, this probe integrates transmit and differential receiver (Tx-dRx) coils, aiming to enhance detection sensitivity and minimise the lift-off impact. The study optimises ECT probes employing different transmitter coils, emphasising three main objectives: (a) quantitatively evaluating each probe using signal-to-noise ratio (SNR) and outlining a real-time data-processing algorithm based on SNR methodology; (b) exploring the frequency range proximal to the electrical resonance of the receiver coil; and (c) examining sensitivity variations across varying lift-off distances. The experimental outcomes indicate that the newly designed probe with a figure-8 shaped transmitter coil significantly improves sensitivity in detecting surface cracks on railroads. It achieves an impressive SNR exceeding 100 for defects with minimal dimensions of 1 mm in width and depth. The simulation results closely align with experimental findings, validating the investigation of the optimal operational frequency and lift-off distance for selected probe performance, which are determined to be 0.3 MHz and 1 mm, respectively. The realisation of this project would lead to notable advancements in enhancing railroad safety by improving the efficiency of crack detection.

Analysis of Cement properties using Hyperspectral Remote Sensing methods

Abstract. Understanding building material properties can play a key role in analyzing the health of structural elements in buildings. However, the field and laboratory tests can be laborious, time-consuming, and cost-intensive. Generally, the standard tests employed for characterizing the material properties are destructive in nature, but in the recent past, non-destructive testing (NDT) has given us the ease of understanding the material properties. Hyperspectral remote sensing technology has been exploited as one of the NDT methods for assessing the properties of building components. This study aims to investigate the quality information about cement using the hyperspectral remote sensing method and correlate it with the results of traditional quality testing methods. The materials used in the experiments included Ordinary Portland Cement (OPC), Portland Pozzolana Cement (PPC), and White Portland Cement (WPC). The cement samples were studied and experimented with for the identification of their types, and their temporal variability and changes in their spectral properties on which the strength of the samples depended were further checked. The study concluded that for OPC, PPC, and WPC, the absorption range is 1970 nm to 2020 nm, which is mainly due to the presence of different chemical compounds in cement. Also, the spectral reflectance decreases with time, and it can be concluded that in the present work, the strength of cement decreases with the decrease in spectral reflectance. Hence, Cement properties can be found using hyperspectral remote sensing data.

Modeling and benchmarking XRF analysis using MCNP for applications in accident tolerant fuel and cladding

There is an interest in using nondestructive testing (NDT) methods for the preliminary investigation of accident-tolerant fuel cladding materials, such as chromium (Cr) coated Zircaloy-4 (Zr4). One promising application is X-ray fluorescence (XRF) analysis. Computational methods, such as Monte Carlo N-Particle Transport (MCNP) 6.2, can be used to expand algorithms based on XRF measurements, however, it has been demonstrated that MCNP is more sensitive to modeling imperfections at lower energies (≤ 80 keV). In this work, several MCNP models were developed to evaluate the XRF measurements given by a Niton XL-5 device to minimize deviations at low energies. The final model was benchmarked to an experimental XRF measurement of Cr-coated Zr4 taken by the XL-5. The percent error in the resulting XRF peak intensities was within ± 4.92% for the Kα1 peaks and within ± 16.0% for the Kβ1. The discrepancies in the magnitude of these errors are largely due to the Kβ1 peaks having far fewer counts in the spectra that were compared. Nonetheless, these results demonstrate the potential for MCNP 6.2 to accurately predict low-energy X-ray interactions such as XRF. The deviations observed were similar to those seen in the 0.1–1 MeV range in prior works, despite only being in the ∼ 5–30 keV range themselves.

A Comparative Study of Non-Destructive Testing Techniques: Active Thermography versus Shearography for 3D-Printed Thermoplastic Composites Reinforced with Continuous Carbon Fiber

This study investigates the feasibility and effectiveness of two non-destructive testing methods, active thermography and shearography, on 3D-printed thermoplastic (TP) composites reinforced with continuous carbon fiber. Artificial defects were introduced into the composite plate to benchmark the detection capabilities of these non-destructive testing techniques (NDT). Active thermography produced a thermogram that highlighted defects through variations in surface temperature. Although effective for identifying defects ranging from 3 to 10 mm in size at four different depths, specifically 1 mm, 1.25 mm, 1.5 mm, and 1.75 mm, through the thickness of a 2.8 mm plate, the method encountered some limitations. It faced challenges in detecting deeper defects and accurately determining their shapes. Shearography, which utilizes fringe pattern distortions to detect surface displacement anomalies, successfully identified near-surface defects within the same size range. However, it required more expertise for accurate interpretation and struggled with detecting smaller and deeper defects. The complementary strengths and limitations of these methods suggest that employing both could offer a more comprehensive solution for defect detection in 3D-printed TP composites.

Ultrasonic Weld Quality Inspection Involving Strength Prediction and Defect Detection in Data-Constrained Training Environments.

Welding is an extensively used technique in manufacturing, and as for every other process, there is the potential for defects in the weld joint that could be catastrophic to the manufactured products. Different welding processes use different parameter settings, which greatly impact the quality of the final welded products. The focus of research in weld defect detection is to develop a non-destructive testing method for weld quality assessment based on observing the weld with an RGB camera. Deep learning techniques have been widely used in the domain of weld defect detection in recent times, but the majority of them use, for example, X-ray images. An RGB image-based solution is attractive, as RGB cameras are comparatively inexpensive compared to X-ray image solutions. However, the number of publicly available RGB image datasets for weld defect detection is comparatively lower than that of X-ray image datasets. This work achieves a complete weld quality assessment involving lap shear strength prediction and visual weld defect detection from an extremely limited dataset. First, a multimodal dataset is generated by the fusion of image data features extracted using a convolutional autoencoder (CAE) designed in this experiment and input parameter settings data. The fusion of the dataset reduced lap shear strength (LSS) prediction errors by 34% compared to prediction errors using only input parameter settings data. This is a promising result, considering the extremely small dataset size. This work also achieves visual weld defect detection on the same limited dataset with the help of an ultrasonic weld defect dataset generated using offline and online data augmentation. The weld defect detection achieves an accuracy of 74%, again a promising result that meets standard requirements. The combination of lap shear strength prediction and visual defect detection leads to a complete inspection to avoid premature failure of the ultrasonic weld joints. The weld defect detection was compared against the publicly available image dataset for surface defect detection.

Modelling and experimental study on pipeline defect characterisations using a pulsed eddy current measurement

ABSTRACT Pipelines are widely used as the main transportation method in petrochemical and metallurgical industries. However, the special nature of the materials transported in pipelines means they are often subjected to high temperature, high pressure and corrosive conditions. This can lead to defects, such as thinning and cracking, resulting in pipeline failure and causing serious economic losses. Therefore, regular non-destructive testing of pipelines is particularly important. This paper uses the non-destructive testing method with pulsed eddy current technology to characterise pipeline thinning and defects. A preliminary study is conducted on the pulsed eddy current detection signal patterns for pipeline crack defects with varying orientations and quantities. A more portable pulsed eddy current detection device with an extended wall thickness detection range is developed. It can achieve measurements with a maximum thickness of 30 mm for detections of pipelines with rectangular defects and thickness-stepped reduced. The experimental results are consistent with modelling results, and the accuracy for pipelines with insulation layer is also investigated. On-site measurements using the PEC system are conducted on equipment sections with pipe thickness within 20 mm in a petrochemical plant. It is confirmed that the developed PEC inspection device can accurately characterises the thickness of pipelines with 0–50 mm cladding.

Fast concrete crack depth detection using low frequency ultrasound array SH waves data

Fast detection of the depths of surface-open cracks plays an important role in evaluating the damage conditions of concrete elements. The presence of surface-open cracks and other anomalies inside concrete complicates the ultrasonic wave field and thus severely undermines the precision of traditional nondestructive testing methods. This study introduces independently developed low-frequency ultrasonic array detection equipment. The detector adopts a doubled-ray coverage strategy to enhance the imaging stability under noisy conditions. Moreover, we propose an imaging method called the crack focusing-synthetic aperture focusing technique (CF-SAFT), through which both reflected and transmitted surface waves are removed so that only diffracted SH waves converge to their origins. An extra instantaneous phase analysis is supplemented to highlight the diffraction points. We test the effectiveness of our method through a multitude of numerical examples and a model experiment. Successful depth identification was obtained regardless of different geometries of the cracks or interference from the steel reinforcements. The superiority of our method is further verified through noisy ultrasonic data and complex scenarios.