Nondestructive Testing Procedures Research Articles

Quantitative Detection of Defects in Multi-Layer Lightweight Composite Structures Using THz-TDS Based on a U-Net-BiLSTM Network.

Multi-layer lightweight composite structures are widely used in the field of aviation and aerospace during the processes of manufacturing and use, and, as such, they inevitably produce defects, damage, and other quality problems, creating the need for timely non-destructive testing procedures and the convenient repair or replacement of quality problems related to the material. When using terahertz non-destructive testing technology to detect defects in multi-layer lightweight composite materials, due to the complexity of their structure and defect types, there are many signal characteristics of terahertz waves propagating in the structures, and there is no obvious rule behind them, resulting in a large gap between the recognition results and the actual ones. In this study, we introduced a U-Net-BiLSTM network that combines the strengths of the U-Net and BiLSTM networks. The U-Net network extracts the spatial features of THz signals, while the BiLSTM network captures their temporal features. By optimizing the network structure and various parameters, we obtained a model tailored to THz spectroscopy data. This model was subsequently employed for the identification and quantitative analysis of defects in multi-layer lightweight composite structures using THz non-destructive testing. The proposed U-Net-BiLSTM network achieved an accuracy of 99.45% in typical defect identification, with a comprehensive F1 score of 99.43%, outperforming the CNN, ResNet, U-Net, and BiLSTM networks. By leveraging defect classification and thickness recognition, this study successfully reconstructed three-dimensional THz defect images, thereby realizing quantitative defect detection.

Eddy current array developments for remote deployment on the ITER project

The inspection of welds and components for nuclear applications can be more challenging due to extreme levels of radiation and temperature. A development project in collaboration with the UK Atomic Energy Authority (UKAEA) is being conducted to develop non-destructive testing (NDT) procedures for inspecting the remotely welded joints within the ITER facility. Initially, a feasibility study has been carried out to identify the most suitable non-destructive technique. Eddy current array (ECA), phased array ultrasonics and guided wave electromagnetic acoustic transducers (EMATs) were considered as candidate solutions and, following the feasibility study, ECA was selected to be taken forward for further development. Subsequently, dedicated high- and low-frequency array probes have been developed and tested to detect and size surface and buried defects employing encoder and calibration curves. The research has been extended to characterise defects such as tungsten inclusions using the phase angle. To avoid damage to the electronics in extreme environments, the ECA probe coils were separated from the excitation and data acquisition unit with 60 m coaxial coils to achieve a high signal-to-noise ratio. The results revealed that the ECA technique can successfully be deployed remotely for the detection and sizing of defects down to 3 mm in extreme environments.

Eddy Currents Assessment of Rail Cracks Using Artificial Neural Networks in a Laboratory Setup

Although flaws associated with rolling contact fatigue (RCF) and the corresponding traffic induced damage, which are a cause of failure in railways, have been of great concern in railway system maintenance and safety strategies in many countries for at least two decades, this serious problem has not been yet adequately tackled in the Argentine railway system. The present upgrading activities undertaken in the Argentine railway system (in infrastructure and in rolling stock) are prompting the need for R&D in non-destructive testing techniques and procedures, to satisfy requirements of the new rolling stock and to ensure safe and economic operation of passengers and cargo. RCF damage appears as surface and near surface defects and grows into cracks which in time will propagate along the running surface and through the cross-section. Eddy current testing (ET) is a very efficient in-service inspection method for this task, the near field technique being especially recommended for ferromagnetic components.
 In the present paper, an artificial neural network (ANN) method for automatic classification of flaws with lift-off compensation is presented and tested. The tests consist on the ET evaluation of right angle artificial cracks on a rail calibration coupon; the depth of the cracks studied ranged from 1 to 7 mm. The technique permitted to compensate the weakening of the signals caused by the lift-off effect, allowing signal cracks classification with lift-off variations of up to 5.4 mm.
 The effect of crack skewness on the ET signals is also studied. Because the RCF cracks penetrate the rail at oblique angles, (10° to 30° to the rolling surface), an additional uncertainty component is added to the experiments if calibration is made with a piece having perpendicular cracks. In order to estimate this additional uncertainty on the ANN method presented here, further tests were made with a second calibration piece with cracks at 25° to the surface. Comparison of results showed that the peak to peak amplitudes for both types of cracks are not equivalent at all the tested depths.

Innovative Applications of AI, Machine Learning, and Smart Technologies in Non destructive Testing (NDT)

Several industries use non-destructive testing (NDT) methods to check the properties of parts and structures for flaws and oddities without affecting the part's stability or causing damage to the part being checked. It involves surveillance. Situation of the materials (Material State Awareness (MSA)) and the structural health monitoring (SHM). Using NDT procedures can help eliminate potential losses and risks associated with component failures while reducing both money and time by not sacrificing its reliability and upcoming utilization. On the other hand, machine learning (ML) and artificial intelligence (AI) techniques are practical instruments that can aid in automated data collection and analysis, offering fresh insights and perhaps enhancing detection efficiency at a high cost quickly and easily. The present research provides an overview of modern AI-ML NDT algorithms and their applications of associated with smart technologies, such as digital twins and machine vision (MV).

Application of Non-destructive Testing in Quality Control of Manufactured Aluminium Metal Matrix Composite Components for the Automotive Industry

The manufacturing of automotive components using aluminium metal matrix composites (Al-MMCs) is progressively gaining a relevant place in the automotive industry. Due to the increasing competition in the automobile sector, there is a great need to supply efficient, stable, and high-quality products. Generally, following automotive standards is voluntary, which means that businesses and other organizations are not legally obliged to apply them. Nevertheless, in certain cases standards may facilitate compliance with legal requirements, such as those contained in European directives and regulations. IATF 16949:2016 is the current version of the IATF 16949 Standard and outlines the requirements an organization must maintain in their quality system for IATF 16949 certification. This new application of manufacturing Al-MMCs requires additional procedures to guarantee the quality of the components manufactured. Because the manufacturing process may generate various defects, such as cracks, voids, lack of fusion, inclusions, porosities, and other metallurgical aspects, non-destructive testing (NDT) techniques should be used for guaranteeing the quality of the components manufactured. The development of reliable NDT methods for aluminium metal matrix composite parts is thus a major challenge. This aspect is related to how NDT procedures need to be optimised or developed to address the singular structures of the processes, such as complex geometry, anisotropic material properties, special internal structures, and the presence of nanoparticles. The main objective of this work is to summarize and present the relevant potential of different NDT techniques used in quality control of manufactured Al-MMC components in the automotive industry, to be explorer in the European FLAMINGo project (Fabrication of Lightweight Aluminium Metal Matrix Composites and Validation in Green Vehicles). This study is very significant because the NDT techniques present different probabilities of detection of the defects, principally of the microdefects.

Understanding the efficiency of autogenous and autonomous self-healing of conventional concrete mixtures through mechanical and microscopical analysis

Among the ways to counter the effects of concrete cracking, self-healing appears to be very promising. This research aims to appraise the effect of autogenous and autonomous self-healing on the condition recovery of mechanically loaded concrete specimens proportioned with distinct raw materials. A commercially available crystalline admixture (CA), different types of SCMs, and chemically modified versions of CA were combined with cement to proportion distinct mixtures. Ninety-six specimens from each mixture were manufactured and cured for 28 and 180 days at 20 °C and 100 % RH, after which they were pre-loaded using up to 90 % of their corresponding compressive strength and restored at the same curing conditions for 90 days. Non-destructive, microscopic, and mechanical test procedures were conducted on the samples after the secondary curing; results indicate that SCMs enhanced the natural healing. However, mixtures to which CA was added demonstrated the highest healing ratios, indicating promising recovery of mechanical properties.

Non-destructive evaluation of GTAWA, GTAWF and LBWA welded Ti6Al4V alloy

Ti6Al4V is the most commonly used titanium alloy which features high strength with low density, high fracture toughness, excellent resistance against corrosion and good weldability. Ti6Al4V finds wide application in variety of sectors such as aircraft structure, jet engines, gas turbines, defence armour and IC engine components. The quality of the titanium weldments is significant in producing titanium structures and components. This requires critical Non-Destructive Testing (NDT) procedures to be followed. There is very scarce literature reported on NDT of titanium weldments. In this investigation the Non-Destructive Testing of Ti6Al4V weldments welded using Gas Tungsten Arc Welding – Autogenous (GTAWA), Gas Tungsten Arc Welding – with filler (GTAWF) and Laser Beam Welding – Autogenous (LBWA). The weldments were subjected NDT procedures such as visual and X-ray radiography. The uniqueness of this study is that the Image Quality Indicator (IQI) of aluminium was used as the IQI for titanium is not available in the industries. Generally, the titanium gets contaminated by gases in the atmosphere, leading to loss of mechanical strength and corrosion resistance of the weldment. Hence in this investigation a novel approach for ruling out the contamination of the weld pool and the Heat Affected Zone (HAZ) has been proposed viz. Weld Quality Indicator (WQI), i.e. the discolouration on the surface of the weldment is taken as an indicator of the contamination.

Fibre volume fraction screening of pultruded carbon fibre reinforced polymer panels based on analysis of anisotropic ultrasonic sound velocity

Composites have become the material of choice in a wide range of manufacturing applications. Whilst ultrasound inspection is a well-established non-destructive testing (NDT) technique, the application to composite imaging presents significant challenges stemming from the inherent anisotropy of the material. The fibre-volume fraction (FVF) of a composite plays a key role in determining the final strength and stiffness of a part as well as influencing the ultrasonic bulk velocity.In this work, a novel FVF determination technique, based on the angular dependence of the sound velocity with respect to the composite fibre direction, is presented. This method is introduced and validated by inspection of pultruded carbon fibre reinforced polymer (CFRP) panels commonly used in the manufacture of high-power wind turbine blades. Full matrix capture (FMC) data acquired from a phased array (PA) ultrasonic probe is used to generate calibration data for samples ranging in FVF from 60.5% to 69.9%. Sample velocity, as a function of propagation angle, is used to estimate the FVF of samples and ensure they fall within the desired range. Experimental results show values of 61.1, 66.1 and 68.3%, comparing favourably to the known values of 60.5, 66.3 and 69.9% respectively.The work offers significant potential in terms of factory implementation of NDT procedures to ensure final parts satisfy standards and certification by ensuring any FVF inconsistencies are identified as early in the manufacturing process as possible.

The Ultrasonic Pulse Velocity and Lagrangian Approaches to Predict the Effective Thickness and Homogeneity of the Sandwich Panel

Non-destructive testing can be applied to various things, including sandwich panels. Sandwich panels made of EPS are greatly affected by the mixing process. Bad mixing can affect the level of homogeneity and reduce quality. In addition, the improper thickness of layers and cores can result in wall damage. For this reason, carrying out a non-destructive test on the sandwich panel is necessary. This study aims to determine the homogeneity of the material and predict the dimensions of the EPS core and layer. Experimental testing was conducted using Ultrasonic Pulse Velocity (direct method) with 90 points. The test object consisted of six sandwich panel walls with three variants, each with dimensions and layer thickness of 15 mm, 20 mm, and 25 mm, respectively, while the core layer size was 70 mm, 80 mm, and 90 mm, respectively. The test results were analyzed on travel time and wave velocity using a statistical analysis approach including covariance, Kolmogorov-Smirnov, ANOVA, t-test, and Lagrangian. The analysis results show that the mixture's homogeneity can be determined based on the ultrasonic pulse velocity. The proposed Lagrange analysis can reveal the behavior of the propagation speed. Based on the results of the Lagrange approach, the highest speed is obtained at a thickness of 80 with a maximum speed of 2.395 km/s. The results of this study contribute to the non-destructive test procedure, especially in determining homogeneity and the dimensions of the effective thickness of the structural walls (cores and layers) that have been installed in the field quickly, cheaply, accurately, and briefly.

Digital Building Diagnostics

Abstract Englisch Many manual steps are usually required in the acquisition, evaluation and presentation of building diagnostic results. In the case of questions with practical relevance several non-destructive testing methods and conventional building diagnostic are customary used. The data obtained are heterogeneous and available in a wide variety of types and formats, including as manual records in measurement protocols or digitally stored on various measuring devices as well. As part of the research project "Digital Building Diagnostics", the project partners (University of Applied Sciences Erfurt, Marx Krontal Partner and Bau-Consult Hermsdorf) are developing methods and procedures to digitize the sub-processes of non-destructive building diagnostics procedures on concrete structures like GPR, ultrasound echo among others and, if possible, to automate them. Essential information is to be digitally recorded with a recording tool and stored in a database already on the building. In the office, further measurement data from the non-destructive test procedures are transferred to the database with the help of a converter. Imaging processes are evaluated by experienced engineers and the results are also stored in the database. With the help of an evaluation tool, data from the database is aggregated in a semi-automated manner and evaluated with subject-specific algorithms. The individual results and the evaluations are located and visualized in a three-dimensional building model. Through the representation in three-dimensional space, connections can be better recognized, and more precise conclusions can be drawn.

Flaw classification and detection in thin‐plate structures based on scaled boundary finite element method and deep learning

AbstractThe identification of internal structural flaws is an important research topic in structural health monitoring. At present, structural safety inspections based on nondestructive testing procedures mainly focus on qualitative analysis; hence, it is difficult to identify the scale of flaws quantitatively. In this article, an inversion model that can realize quantitative detection is proposed by combing the scaled boundary finite element method (SBFEM) with deep learning. First, the lamb wave propagation processes in thin structures containing flaws were simulated using the SBFEM, and the echo wave signal at an observation point was recorded and paired with the flaw information. Then, the paired SBFEM datasets were used to train the deep learning model employing a convolutional neural network. A flaw classification and identification model based on SBFEM datasets was established. For a thin structure with unknown flaw information, and by using the measured observation point signals, the established deep learning model can predict flaw information in real time. Finally, the model performance was verified using several numerical examples. The results show that the proposed model can accurately predict flaw information and is robust against noise; thus, it offers an advantage over existing nondestructive testing procedures and a development in the field of structural health monitoring.

Nondestructive rock porosity estimation by InfraRed Thermography applied to natural stones

The prediction of rock porosity through infrared thermography holds a high potential for non-destructive testing procedures for natural stones, but at the same time there are still few scientific experiences on its applicability on different rock types. In fact, although international standards on laboratory rock characterization allow the use of differently shaped and sized specimens, data available in the literature concern the application of this novel procedure only on cubical rock specimens, with promising results even in the perspective of a potential future standardization of the test. In this paper, motivated by the need for non-destructive analysis of construction and cultural heritage materials, 143 rocks were analyzed to study the reliability of infrared thermography on specimens with different geometries. Results demonstrate that the cooling rate within the first 10 min of test remains the most suitable index for the prediction of rock porosity. This physical property was further analyzed by separately considering the normal and the effective porosities against the rock cooling speed. The best statistical correlations were found for the prediction of total porosity, although satisfactory trends were achieved also for indirectly estimating effective porosity. According to four different sets of specimens, prediction equations were developed from statistical analysis. Achieved outcomes argue strongly for additional scientific research on this prospective test, with the goal to define a standardized, non-destructive, and quick alternative to the common procedures currently used in laboratory for measuring porosity.

A Nondestructive Longitudinal Laboratory Test Method for Detection of Incipient Ultrastructural Changes in Wood

Abstract The established methods for determining the effects of ultrastructural changes in structural wood members rely upon traditional, possibly outmoded, testing paradigms. These methods usually involve destructive testing of wood specimens that are exposed to environmental conditions infrequently experienced in buildings. Understanding how the ultrastructure changes with time within the same specimen is crucial for building practitioners in assessing risks to life-safety, assessing remaining service life of structural components, and aiding in the identification of mitigation or remediation measures. The primary agents causing ultrastructural modifications to wood are wood rotting basidiomycetes but may also include other biotic and abiotic agents; the methods contained herein should be applicable to all such longitudinal experiments. This article reviews existing literature on decay testing and validates a new method to assess longitudinal changes in mechanical properties with time using nondestructive test measures at relevant moisture contents. The validation testing shows this method has a good degree of repeatability and should permit the initial detection and monitoring of ultrastructure changes (e.g., decay). The method uses a repeatable, nondestructive four-point testing procedure for specimens controlled to specific moisture contents using energy dissipation as the salient performance metric. Recommendations are provided to refine this novel test method.

Microstructured induced band pattern in Love wave propagation for novel nondestructive testing (NDT) procedures

We propose a new approach for assessing microstructural properties of materials via nondestructive testing (NDT). This approach lies on the observation that, accounting for the microstructure within the materials, reveals a nonclassical band propagation pattern for Love waves. Precisely this propagation structure may be directly related to the internal microstructure. To illustrate this, propagation of Love waves is first investigated within the linear theory of couple stress materials with micro-inertia. Proving wave existence by the argument principle provides a closed-form condition for propagation to occur. This connection defines propagation bands, whose limits correspond to the situation when Love waves move with the same speed as bulk waves in the underlying half-space (internal resonance). This condition is closely related to the layer-to-substrate microstructure and it may be used to assess either of the two. Furthermore, we show that the frequency equation is a three-term combination of antiplane Rayleigh and Rayleigh–Lamb functions (in a free and in a free/clamped plate). Consequently, investigation of any extra observable, such as Rayleigh waves, reduces the risk of multiple solutions at the signal processing stage. We finally consider the limit as either the half-space or the layer becomes classical elastic. We show that this unseemly bonding of dissimilar models, sometimes adopted in the literature, usually leads to inconsistencies.

Dichotomy property of dispersion equation of guided waves propagating in anisotropic composite plates

Accurate prediction of dispersion curves for ultrasonic guided waves propagation in multi-layered composite laminates is crucial for the deployment of nondestructive testing procedures and structural health monitoring algorithms dedicated to aerospace composite materials. However, existing efforts mainly focused on finding ways to build complex-valued dispersion equations for guided waves (see transfer matrix method (TMM), global matrix method (GMM) and stiffness matrix method (SMM) for example) with little focus on developing efficient and stable numerical solving methods associated with the derived complex-valued equations. In this paper, the conditions under which complex-valued dispersion equations are either real- or purely imaginary-valued equations (termed as dichotomy property) are derived for both single- and multi-layered composite plates. With such a property, the complex-valued dispersion equations can be efficiently numerically solved within the real number field via the standard bisection method or the corrected phase change method. It is thus now possible to overcome numerical issues frequently reported in literature. Besides, a parallel computing technique is proposed in this paper to improve the computational efficiency of the traditional GMM. The proposed methodology provides a new standard framework to solve the dispersion equations which is stable, multipurpose, and numerically efficient.

STUDY OF NEGATIVE INFLUENCE OF PETROLEUM PRODUCTS ON METAL ELEMENTS OF RAILWAY INFRASTRUCTURE

Purpose. The main purpose of the work is to improve the safety of railway transport operation. The implementation of this purpose is provided by assessing and preventing the negative influence of petroleum products on the structural elements of the railway infrastructure. Methodology. The main criterion for assessing the residual life of technical elements of equipment is their strength characteristics. One of the key factors affecting the suitability of technical devices during operation is corrosion damage to the metal. Therefore, the influence of various combinations of the mineralization degree and concentration of petroleum products on the corrosion rate of metals most often used in the design of cooling systems for diesel engines and other transport objects was studied. Thus, for carrying out static and dynamic laboratory tests, model working solutions with different salinity (demineralization model) and different concentrations of dissolved petroleum products (model of standard solutions with organic impurities) were selected. Dynamic and static studies of corrosion damage were carried out by the gravimetric method and the method of polarization resistance using standard samples and solutions. Findings. Based on the results of experimental studies of the authors and analytical data processing, generalizing model dependences of the corrosion rate on the mineralization degree of the working solution of the fractional composition of petroleum products were obtained. A study was carried out and the influence of the water demineralization degree on the corrosion rate of materials of diesel cooling systems was established. Originality. Based on a wide range of author's experimental data, the presence of generalizing dependences of the corrosion rate on temperature, the mineralization degree of the working solution and the fraction of the petroleum products composition has been shown and proven. The obtained dependences form the basis of the developed mathematical model of corrosion, which is a set of relationships linking the characteristics of the corrosion process with various factors influencing its development. Practical value. The obtained experimental and analytical data can be widely used for non-destructive testing procedures, detailed prediction of the state of structural elements and the selection of effective inhibitors to reduce the corrosive aggressiveness of the environment and protect structures.

Broadband nonlinear elastic wave modulation spectroscopy for damage detection in composites

A non-destructive testing procedure is proposed for damage detection in composites using full wavefield measurement obtained using a scanning laser Doppler vibrometer. Vibrations are excited using two low-power piezoelectric actuators leading to nonlinear elastic wave modulation at the defect. One actuator is supplied with a broadband chirp signal and the other actuator is supplied with a single-frequency sine signal. First, a time–frequency filtering method is proposed to extract specific nonlinear components of interest (e.g. second higher harmonic and first modulation sideband) without the need for multiple excitation sequences. Next, damage maps are constructed using broadband bandpower calculation of the filtered nonlinear components. It is demonstrated that the modulation sidebands provide an exclusive imaging of defect nonlinearity, and are not affected by potential source nonlinearity. The proposed damage map construction procedure is applied for various carbon fiber reinforced polymer test specimens with different damage features: (1) coupon with quasi-static indentation damage, (2) coupon with artificial delaminations, (3) bicycle frame with impact damage, and (4) stiffened aircraft panel with partially debonded stiffener. The obtained results indicate the high performance of the developed procedure for detection of various defect types in curved and/or stiffened carbon fiber reinforced polymer components.

Design of a Computed Tomography Automation Architecture

This paper presents a literature review on techniques related to the computed tomography procedure that incorporate automation elements in their research investigations or industrial applications. Computed tomography (CT) is a non-destructive testing (NDT) technique in that the imaging and inspection are performed without damaging the sample, allowing for additional or repeated analysis if necessary. The reviewed literature is organized based on the steps associated with a general NDT task in order to define an end-to-end computed tomography automation architecture. The process steps include activities prior to image collection, during the scan, and after the data are collected. It further reviews efforts related to repeating this process based on a previous scan result. By analyzing the multiple existing but disparate efforts found in the literature, we present a framework for fully automating NDT procedures and discuss the remaining technical gaps in the developed framework.

A machine learning method for defect detection and visualization in selective laser sintering based on convolutional neural networks

Part defects and irregularities that influence the part quality is an especially large problem in additive manufacturing (AM) processes such as selective laser sintering (SLS). Destructive and non-destructive testing procedures are currently mostly used for quality control and defect detection of AM parts after production. In this context, machine learning (ML) algorithms are increasingly being used to enable computer-aided defect detection through automatic classification of manufacturing data. Convolutional neural networks (CNN) based on ML methods are widely used for this task. In this paper, complex transfer learning (TL) methods are presented, which enable the automatic classification of powder bed defects in the SLS process using very small datasets. The proposed methods use the VGG16 and the Xception CNN model with pretrained weights from the ImageNet dataset as initialization and an adapted classifier to classify good and defective image data recorded during part manufacturing. Known performance metrics were determined to evaluate and compare the performance of the models. The VGG16 model architecture achieved the best results for Accuracy (0.958), Precision (0.939), Recall (0.980), F1-Score (0.959) and AUC value (0.982). These results show the effectiveness of defect detection based on CNN and can offer an alternative method for non-destructive quality assurance and manufacturing documentation for additively manufactured parts.

Fast Falling Weight Deflectometer Method for Condition Assessment of RC Bridges

In this paper, the use of Fast Falling Weight Deflectometer (Fast-FWD) is analyzed as a non-destructive and quick test procedure to evaluate the efficiency of short-span bridges. The Fast-FWD is an instrument that can produce a broadband dynamic force up to an impact value of 120 KN: The impact is constant and replicable, providing accurate action measures of bridge stiffness in a truly short period (30 ms). In this paper, a single-span reinforced concrete bridge is investigated, using the Fast-FWD. The considered bridge, approximately 12.0 m long and 15.5 m wide, was in critical condition. The bridge is in a suburban principal road near to the City of Cagliari in Sardinia (Italy), with an Annual Average Daily Traffic of 13,500 vehicles/day, and was suddenly closed, creating serious problems for urban mobility. In these conditions, the investigation through other standard techniques is time-consuming and labor intensive. For this reason, it is important to introduce methods that can be rapid, accurate and cost-efficient. In this paper, bridge stiffness values obtained during the in situ experimental campaign were compared with finite element models values. The Fast-FWD has the potential to provide engineering information that can help us to better understand bridge condition, in a rapid and cost-effective procedure.