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This study aimed to assess the delamination detection in FMLs via the finite element (FE) simulations of Lamb wave propagation. An FE model of an FML specimen with [Al/902/Al/902/Al] layup was developed. Delamination damage of 10 and 25 mm diameters was induced between different layers of the FML specimen. The fundamental antisymmetric Lamb wave mode (A0) at 60 kHz and the fundamental symmetric Lamb wave mode (S0) at the frequency of 206 kHz were propagated on the developed FE models. The Lamb wave phase velocity was obtained from the FE models and compared with those obtained from the Lamb wave propagation tests. The sensitivity of the A0 and S0 Lamb wave modes to the delamination and its diameter were examined. The inverse Lamb wave propagation problem was then solved, and the elastic modulus of the FML specimen was estimated in the intact and delamination regions. It was observed that the phase velocity of the S0 Lamb wave mode had a higher sensitivity to the delamination damage compared to that of the A0 Lamb wave mode. The phase velocity of the A0 Lamb wave mode was more sensitive to the delamination diameter. The capability of the proposed simulated Lamb wave propagation method as a virtual lab for detecting delamination in the FMLs was confirmed.
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This article proposes a deep learning-assisted nondestructive evaluation (NDE) technique for locating and sizing a coating delamination using ultrasonic guided waves. The proposed technique consists of sending a propagating guided wave into a coated plate with a transducer and measuring the corresponding time-domain signals by receivers at several locations at downstream distances from the source transducer. The received time-domain signals are then provided to a trained machine-learning (ML) algorithm, which subsequently outputs the location and size of any delamination flaws between the transducer and receivers. Numerical simulations show that the proposed NDE technique yields accurate results with high throughput, once the ML algorithm is well trained. Although training the ML algorithm is time-consuming, this training only needs to be done once for a given sample configuration. The results of this article demonstrate that the proposed technique has great potential for characterizing delamination flaws in practical NDE and structural health monitoring (SHM) applications.
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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.
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The field of plant phenotype is used to analyze the shape and physiological characteristics of crops in multiple dimensions. Imaging, using non-destructive optical characteristics of plants, analyzes growth characteristics through spectral data. Among these, fluorescence imaging technology is a method of evaluating the physiological characteristics of crops by inducing plant excitation using a specific light source. Through this, we investigate how fluorescence imaging responds sensitively to environmental stress in garlic and can provide important information on future stress management. In this study, near UV LED (405 nm) was used to induce the fluorescence phenomenon of garlic, and fluorescence images were obtained to classify and evaluate crops exposed to abiotic environmental stress. Physiological characteristics related to environmental stress were developed from fluorescence sample images using the Chlorophyll ratio method, and classification performance was evaluated by developing a classification model based on partial least squares discrimination analysis from the image spectrum for stress identification. The environmental stress classification performance identified from the Chlorophyll ratio was 14.9% in F673/F717, 25.6% in F685/F730, and 0.209% in F690/F735. The spectrum-developed PLS-DA showed classification accuracy of 39.6%, 56.2% and 70.7% in Smoothing, MSV, and SNV, respectively. Spectrum pretreatment-based PLS-DA showed higher discrimination performance than the existing image-based Chlorophyll ratio.
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Clorofila , Productos Agrícolas , Clorofila/análisis , Análisis de los Mínimos Cuadrados , Imagen Óptica , FluorescenciaRESUMEN
Thin-walled metallic structures produced by the Directed Energy Deposition (DED) Additive Manufacturing (AM) process are prone to various fabrication defects, which hinder the wider applications of the technique in practice. In-situ inspection and monitoring methodologies are in high demand for improved quality control of printed parts. This paper presents an ultrasonic guided-wave-based method and a prototype that can potentially be used for in-situ inspection of thin-walled structures produced by DED. Lamb waves are excited by a Lead zirconate titanate (PZT) piezoelectric transducer bonded on the DED substrate remotely from the thin wall. The substrate works as a waveguide to transmit the waves which then propagate along the thin wall. A non-contact laser vibrometer is applied to measure the guide wave signals by scanning the surface of the thin wall. The mechanisms of guided wave generation and propagation along the substrate and printed part are theoretically studied. It allows for choosing proper inspection parameters to enhance the measurement sensitivity of guided waves and help interpret the signals for defect detection. Experiments were conducted with DED-produced stainless steel (316L) thin-walled structure. The new method is demonstrated in one example to detect and localize a small defect caused by inconsistent powder delivery of a fabricated thin wall sample, via analysing the B-scan ultrasonic guided wave signals. The new technique provides strong potential for in-situ online monitoring of the DED process.
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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.
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This paper aims to present multisensory spatial analysis (MSA). The method was designed for the quick, simultaneous identification of concrete cover thickness h, rebar diameter, and alloys of reinforcement in large areas of reinforced concrete (RC) structures, which is a complex and unsolved issue. The main idea is to divide one complex problem into three simple-to-solve and based on separate premises tasks. In the transducers designed with the MSA, sensors are arranged spatially. This arrangement identifies each RC parameter separately based on the different waveforms/attributes. The method consists of three steps. All steps are described in the paper and supported by simulations and statistical analysis of the measurement. The tests were carried out using an Anisotropic Magneto-resistance (AMR) sensor. The AMR sensors can measure strong DC magnetic fields and can be combined in spatial transducers because of their small size. The selection of the sensor was extensively justified in the introduction section. The spatial transducer and the identification's simplicity can allow for high accuracy in the real-time area testing of all three parameters. The risk of misclassification of discrete parameters was strongly reduced, and the h parameter can be identified with millimeter accuracy.
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This paper presents the development of the lock-in thermography system with an additional cooling system. System feasibility is tested by investigating a square-shaped glass fiber-reinforced polymer (GFRP) with artificially made outer flaws. The influence of heating mode and sinusoidal excitation period on the defect detectability is considered. Thus, the experiment is split into two modes: the sample is solely heated in the first mode or simultaneously heated and cooled in the second. In each mode, the temperature measurement is performed first with a shorter excitation signal period and second with a longer one. The signal-to-noise ratio (SNR) is used to assess defect detection quantitatively. The comparative analysis shows that employing a mixed heating-cooling mode improves the SNR compared to the conventional heating mode. The further enhancement of the SNR is obtained by extending the excitation period. The combination of simultaneous heating and cooling with longer periods of the excitation signal allows for the best SNR values for the most detected defects.
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This work presents how significantly the proper selection of the magnetization method can improve almost all parameters of the magnetic method and affect the effectiveness of the evaluation of reinforced concrete (RC) structures. Three magnetization methods are considered in this paper: opposite pole magnetization (typical solution), same pole magnetization, and (as a reference point) no magnetization. The experiments are carried out in a three-dimensional (XYZ) space. Measurements along each of the axes are discussed in a separate section. The results show that the appropriate selection of the magnetization method can affect noise reduction, signal strength, and the separation of measurements carried out on different samples. This paper also discusses the situations when the magnetization may change the shape, cause deformations of waveforms, affect the area testing, and be used to significantly increase the efficiency of simultaneous evaluation of three basic parameters of RC structure. Experiments and simulations have proven that properly applied magnetization may strongly affect the evaluation's effectiveness, making the magnetic method one of the most promising techniques in testing RC constructions.
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This study develops a framework for determining the material parameters of layered engineered wood in a nondestructive manner. The motivation lies in enhancing nondestructive evaluation (NDE) and quality assurance (QA) for engineered wood or mass timber, promising construction materials for sustainable and resilient civil structures. The study employs static compression tests, guided wave measurements, and a genetic algorithm (GA) to solve the inverse problem of determining the mechanical properties of a laminated veneer lumber (LVL) bar. Miniature LVL samples are subjected to compression tests to derive the elastic moduli and Poisson's ratios. Due to the intrinsic heterogeneity, the destructive compression tests yield large coefficients of variances ranging from 2.5 to 73.2%. Dispersion relations are obtained from spatial-temporal sampling of dynamic responses of the LVL bar. The GA pinpoints optimal mechanical properties by updating orthotropic elastic constants of the LVL material, and thereby dispersion curves, in a COMSOL simulation in accordance with experimental dispersion relations. The proposed framework can support estimation accuracy with errors less than 10% for most elastic constants. Focusing on vertical flexural modes, the estimated elastic constants generally resemble reference values from compression tests. This is the first study that evaluates the feasibility of using guided waves and multi-variable optimization to gauge the mechanical traits of LVL and establishes the foundation for further advances in the study of layered engineered wood structures.
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The quality control of thermally modified wood and identifying heat treatment intensity using nondestructive testing methods are critical tasks. This study used near-infrared (NIR) spectroscopy and machine learning modeling to classify thermally modified wood. NIR spectra were collected from the surfaces of untreated and thermally treated (at 170 °C, 212 °C, and 230 °C) western hemlock samples. An explainable machine learning approach was practiced using a TreeNet gradient boosting machine. No dimensionality reduction was performed to better explain the feature ranking results obtained from the model and provide insight into the critical wavelengths contributing to the performance of classification models. NIR spectra in the ranges of 1100-2500 nm, 1400-2500 nm, and 1700-2500 nm were fed into the TreeNet model, which resulted in classification accuracy values (test data) of 94.35%, 89.29%, and 84.52%, respectively. Feature ranking analysis revealed that when using the range of 1100-2500 nm, the changes in wood color resulted in the highest variation in NIR reflectance amongst treatments. As a result, associated features were given higher importance by TreeNet. Limiting the wavelength range increased the significance of features related to water or wood chemistry; however, these predictive models were not as accurate as the one benefiting from the impact of wood color change on the NIR spectra. The developed framework could be applied to different applications in which NIR spectra are used for wood characterization and quality control to provide improved insights into selected NIR wavelengths when developing a machine learning model.
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The magnetic method is one of the very few nondestructive testing (NDT) techniques that provide the possibility to conduct area tests of reinforced concrete (RC) structures in a fast, cheap, and straightforward way. This paper aims to present a new approach to the simultaneous identification of rebars' diameter, alloy class, and thickness of the concrete cover tested with this method. Since rebars from different manufacturers may have different electromagnetic properties (standardization only for mechanical properties), preparing an effective and universal database is impossible. In this work, ACO decomposition is proposed, a new attributes extraction method designed to identify object parameters, even if it is impossible to collect a suitable training database (by pattern recognition and analysis of the deviation). Conducted tests prove that the ACO method enables accurate reflection of the waveform shape and limitation of attributes number to three or fewer (avoiding the curse of dimensionality). These properties, combined with the ability to analyze spatial components of magnetic induction (which only magnetic sensors provide), make the complex task of identification of three parameters more straightforward and the separation between the results received for different classes larger. This article presents the measurement results and the whole identification process.
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An ultrasonic nondestructive evaluation procedure is presented for the ply and interleaf layer properties of carbon/epoxy composite laminates having interleaf resin layers at their interlaminar interfaces. It is shown that the material properties of plies (density, thickness, and transversely isotropic complex elastic moduli) and elastic interleaf layers (density, thickness, Young's modulus, and Poisson's ratio) can be identified by best fitting the theoretical energy transmission spectra of longitudinal waves through the laminate immersed in water to those obtained in ultrasonic measurement. It is also demonstrated that compared to the mass-less and null-thickness spring-type interlaminar interface model employed in the previous works, the present finite-thickness interleaf layer model can better reproduce the experimental transmission spectra of a unidirectional interleaved composite laminate containing ultrasonic bandgaps. Furthermore, the property characterization in the case where the plies and interleaf layers are assumed to be elastic and viscoelastic, respectively, is also examined. It is also shown that the ply and interleaf layer properties determined for the unidirectional laminate can be used to reproduce the transmission spectra measured for a quasi-isotropic laminate consisting of the same plies and interleaf layers.
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The integration of flexible electronics with optics can help realize a powerful tool that facilitates the creation of a smart society wherein internal evaluations can be easily performed nondestructively from the surface of various objects that is used or encountered in daily lives. Here, organic-material-based stretchable optical sensors and imagers that possess both bending capability and rubber-like elasticity are reviewed. The latest trends in nondestructive evaluation equipment that enable simple on-site evaluations of health conditions and abnormalities are discussed without subjecting the targeted living bodies and various objects to mechanical stress. Real-time performance under real-life conditions is becoming increasingly important for creating smart societies interwoven with optical technologies. In particular, the terahertz (THz)-wave region offers a substance- and state-specific fingerprint spectrum that enables instantaneous analyses. However, to make THz sensors accessible, the following issues must be addressed: broadband and high-sensitivity at room temperature, stretchability to follow the surface movements of targets, and digital transformation compatibility. The materials, electronics packaging, and remote imaging systems used to overcome these issues are discussed in detail. Ultimately, stretchable optical sensors and imagers with highly sensitive and broadband THz sensors can facilitate the multifaceted on-site evaluation of solids, liquids, and gases.
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Shot peening is a process wherein the surface of a material is impacted by small, spherical metal shots at high velocity to create residual stresses. Nickel-based superalloy is a material with high strength and hardness along with excellent corrosion and fatigue resistance, and it is therefore used in nuclear power plants and aerospace applications. The application of shot peening to INCONEL, a nickel-based superalloy, has been actively researched, and the measurement of residual stresses has been studied as well. Previous studies have used methods such as perforation strain gauge analysis and X-ray diffraction (XRD) to measure residual stress, which can be evaluated with high accuracy, but doing so damages the specimen and involves critical risks to operator safety due to radiation. On the other hand, ultrasonic testing (UT), which utilizes ultrasonic wave, has the advantage of relatively low unit cost and short test time. One UT method, minimum reflection measurement, uses Rayleigh waves to evaluate the properties of material surfaces. Therefore, the present study utilized ultrasonic minimum reflectivity measurements to evaluate the residual stresses in INCONEL specimens. Specifically, this study utilized ultrasonic minimum reflection measurements to evaluate the residual stress in INCONEL 718 specimens. Moreover, an estimation equation was assumed using exponential functions to estimate the residual stress with depth using the obtained data, and an optimization problem was solved to determine it. Finally, to evaluate the estimated residual stress graph, the residual stress of the specimen was measured and compared using the XRD method.
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Crack damage is one of the significant factors that may accumulate at the stress concentration area of engineering structures and cause catastrophic accidents. In this paper, we proposed a novel approach to identify the crack location and size by exploiting the reflections and diffractions of Lamb waves. The interaction mechanism between the crack and Lamb wave has been analyzed thoroughly, our analysis of the interaction between the crack and Lamb wave revealed that both reflections and diffractions carry valuable damage information that reflects the size and orientation of the crack. As the interaction coefficients between these two components and the crack are different, there are supposed to exist differences between them in the amplitude value. We implemented a threshold to classify the signals received from all paths into two groups: reflections and diffractions. Then we constructed an overcomplete dictionary of waveforms corresponding to different propagation distances to extract the damage information. Using sparse decomposition, the received signals were mapped to their corresponding propagation distances without the use of baseline signals. The diffractions allow us to determine the crack's tip points, while the reflections provide information about the edge points. The kinked crack's size and orientation were visualized based on the time-domain signals acquired in our experiment. We provided a comprehensive description of our algorithm and verified it through numerical simulation and experimental data. Our results show high agreement with actual cracks, demonstrating the efficacy of our proposed method.
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A feasibility study is presented on the experimental application of ultrasound to examine rolled stainless steel plates having equidistant surface textures in two directions in the form of Penrose tiles. The specific problem of interest is investigating surface profile quality in terms of its equidistance and depth to monitor the manufacturing process. The goal is to eventually replace current time-consuming optical examination procedures with a reliable and rapid ultrasonic inspection technique. Two practical experimental setups are discussed and compared in this work: examining frequency spectra obtained from normal incidence pulse-echo measurements, and those obtained at Laue angle incidence. A thorough survey of ultrasonic methods precedes the experimental results to investigate such surfaces from a historical perspective.
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Two test series were examined using nondestructive measuring methods by six independent laboratories before determining their compressive strength. The nondestructive test methods used were the rebound hammer and ultrasonic pulse velocity measurement. Two types of geometries were investigated: drilled cores and cubes. The measurement procedure for each of these datasets is conditioned to the geometry and is therefore different. The first series consists of 20 drilled cores (approximately diameter/height = 10 cm/20 cm) from the 55-year-old Lahntal Viaduct near Limburg, Germany. After preparation in the first laboratory, the lateral surface of the drilled cores was tested with the rebound hammer using a given pattern. Every laboratory tested every drilled core at different locations. Ultrasonic measurements in transmission were performed repeatedly at predefined points on the flat surfaces of the specimen. The second series consisted of 25 newly manufactured concrete cubes of a mix with a target concrete strength class of C30/37. The edge length was 15 cm. Each laboratory received five specimens of this test series. Thus, contrary to the first series, each specimen was tested by only one laboratory. Two side faces of each cube were tested with the rebound hammer. In addition, ultrasonic measurements were performed by one laboratory. The time of flight was measured between the tested side faces of the rebound hammer at different positions. For both series, rebound hammers were used to determine the R-value as well as the Q-value. The rebound hammer models within the laboratories were always the same, while they differed between the laboratories. The ultrasonic measurements took place with different measurement systems and couplants. Finally, both specimen series were tested destructively for compressive strength. The dataset contains the raw data summarized in tabular form. In addition, relevant calculated data are included in some cases. For the ultrasonic measurements, the time of flight has already been converted into the ultrasonic velocity. Besides, in addition to the raw data of the compressive strength test (force, weight, and geometry values), the calculated compressive strengths and densities are also provided.
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The inspection of structures operating at high temperatures is a major challenge in a variety of industries, including the energy and petrochemical industries. Operators are typically performing nondestructive evaluations using ultrasound to monitor component thicknesses during scheduled shutdowns, thereby ensuring safe operation of their plants. However, despite being costly, this calendar-based approach may lead to undetected corrosion, which can potentially result in catastrophic failures. There is therefore a need for ultrasonic transducers designed to withstand permanent exposure to high temperatures, so as to continuously monitor the remnant thicknesses of structures in real time. This paper discusses the design of a heat-resistant ultrasonic transducer based on a piezoelectric element. The piezoelectric material, the electrodes, the backing layer, the wires and the casing are presented in detail from the acoustic and thermal expansion point of view. Four transducers optimized for 3 MHz were manufactured and tested to destruction in different conditions: (1) 72-h temperature steps from room temperature to 750 ∘C, (2) thermal cycles from room temperature to 500 ∘C and (3) 60 days of continuous operation at >550 ∘C. The paper discusses the results, as well as the effect of temperature over time on the properties of the transducer.
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A compact and planar imaging system was developed using a flexible polymer substrate that can distinguish subcutaneous tissue abnormalities, such as breast tumors, based on electromagnetic-wave interactions in materials where permittivity variations affect wave reflection. The sensing element is a tuned loop resonator operating in the industrial, scientific, and medical (ISM) band at 2.423 GHz, providing a localized high-intensity electric field that penetrates into tissues with sufficient spatial and spectral resolutions. The resonant frequency shifts and magnitudes of the reflection coefficients indicate the boundaries of abnormal tissues under the skin due to their high contrasts to normal tissues. The sensor was tuned to the desired resonant frequency with a reflection coefficient of -68.8 dB for a radius of 5.7 mm, with a tuning pad. Quality factors of 173.1 and 34.4 were achieved in simulations and measurements in phantoms. An image-processing method was introduced to fuse raster-scanned 9 × 9 images of resonant frequencies and reflection coefficients for image-contrast enhancement. The results showed a clear indication of the tumor's location at a depth of 15 mm and the capability to identify two tumors both at the depth of 10 mm. The sensing element can be expanded to a four-element phased array for deeper field penetration. Field analysis showed the depths of -20 dB attenuation were improved from 19 to 42 mm, giving wider coverage in tissues at resonance. Results showed that a quality factor of 152.5 was achieved and a tumor could be identified at a depth of up to 50 mm. In this work, simulations and measurements were conducted to validate the concept, showing great potential for subcutaneous imaging in medical applications in a noninvasive, efficient, and lower-cost way.