Nondestructive Damage Evaluation Research Articles

Enhanced detection of fine damage in composite materials using integrated X-ray computed tomography, digital volume correlation, and U-Net

Accurate detection of fine damage in composite materials is essential for advancing research and engineering practices in various industries. This study introduces a novel method for detecting fine damage by integrating Digital Volume Correlation (DVC) and U-Net. X-ray computed tomography (XCT) images are analyzed to correlate microstructural features with damage progression. DVC residuals highlight new damage, while U-Net enhances segmentation precision. This approach addresses challenges in differentiating inherent flaws from new cracks, improving detection accuracy. Experimental validation and synthetic data generated by the discrete element method (DEM) demonstrate the method's robustness. The results show that the method is susceptible to newly formed microcracks and can accurately identify fine cracks, with over 60% being nearly invisible in XCT images. Cyclic loading experiments on two mortar samples reveal that cracks are the predominant form of damage, exhibiting different evolution characteristics during the loading process. The method significantly advances the non-destructive damage evaluation of composite materials.

Non-destructive damage evaluation of composites based on X-ray and ultrasonic image fusion

Non-destructive damage evaluation of composites based on X-ray and ultrasonic image fusion

Compression–compression fatigue damage of wrinkled carbon/glass hybrid composite laminates

Compression–compression fatigue tests were carried out to study the compressive fatigue damage mechanisms of a carbon/glass hybrid composite laminate with a manufacturing-induced wrinkle defect. As reported in literature, thin wrinkled composite laminates could show sensibly different fatigue behaviours and damage mechanisms in the presence of tension and compression cyclic loadings. The sensitivities of composites to tension and compression cyclic loadings were not the same. In this study, the damage modes and cracking sequence of a thick wrinkled laminate were identified by the strain fields from digital image correlation (DIC). Acoustic emission (AE) and infrared (IR) thermography were used to detect the damages and explore the potential capabilities of non-destructive evaluation methods when applied to detection of fatigue induced damages in composite structures under operational cyclic loadings. Two primary damage modes were identified and detected before the final failure of the laminate i.e. debonding between the resin-rich layer and the laminate and interlaminar cracks. Debonding occurred earlier than interlaminar cracking. Out-of-plane stresses due to fibre waviness drove the initiation of these damages. Under the settings of AE in this work, AE captured early debonding activities but did not detect the micro damage accumulating before the formation of interlaminar crack. Interlaminar crack initiation was tracked by IR thermography at cross-section of the specimen based on the distinct temperature localisations that occur with this micro damage. No distinct temperature localisations occurred during the formation of debonding as it is mode-I driving under compression–compression cyclic loading, so IR thermography did not detect the debonding crack. The results of this study show the potential of using complementary non-destructive damage evaluation methods to inspect early damages in composite structures. The early detection of damages would give early warning signs before the damages become critical. It is found that AE and IR thermography should be used as complementary tools to detect different damage modes.

Interfacial debonding detection for steel-concrete composite structures part I: Benchmark test and signal calibration of contact and non-contact measurement

Interfacial bonding state is one of the crucial factors affecting the bearing capacity and durability of steel-concrete composite structures (SCCS). To achieve efficient interfacial debonding detection for SCCS, contact and non-contact method-based novel non-destructive test (NDT) is performed in this study. In order to figure out the dominant frequency and vibration mode of steel plate above interfacial debonding defects in SCCS, systematic theoretical analysis and finite element simulation are carried out. Then, the contact NDT experimental study is carried out utilizing piezoelectric lead zirconate titanate (PZT) sensors and acceleration meters. Conventional and high-frequency microphones, as well as high-precision scanning laser Doppler vibrometer (SLDV), are introduced for non-contact measurement. According to benchmark test on steel plate-bonded reinforced concrete cube with preset interfacial debonding defects in different dimensions, the signal calibration with Pearson correlation coefficient is further performed to quantitatively assess the variation and difference between contact and non-contact measurements. Research findings indicate that the synthetic experimental testing platform integrating contact vibration measurement, non-contact SLDV, and acoustic vibration measurement established in this study is capable of performing non-destructive tests using impact-response method, impact-echo technique, impact-acoustic vibration approach, and modal test simultaneously. Moreover, SLDV can precisely obtain the vibration mode of steel plates above interfacial debondings. Aided by high-precision scanning laser-based vibration measurement, the accuracy and efficiency of in-situ non-contact tests can be improved. The research findings in this study can provide practical guidance on non-destructive evaluation of interfacial damages in SCCS.

Nonlinear autoregression-based non-destructive evaluation approach for railway tracks using an ultrasonic fiber bragg grating array

The safe operation of railway systems relies on rapid and accurate non-destructive damage detection and evaluation techniques to help rule out potential threats brought by rail track damages. In this paper, an innovative fiber Bragg grating (FBG) array-based sensing system is developed for rail defect inspection by integrating the ultrasonic testing technology, and a nonlinear autoregressive neural network with exogenous inputs (NARX) is proposed for rail damage detection based on measured ultrasonic signals. The developed sensing system can realize the high-speed and multiplexed ultrasonic signals demodulation. The NARX is proposed to process the ultrasonic signals obtained by the developed sensing system, and a specified probabilistic damage sensitive feature (DSF) is tailored to indicate rail health status, derived from the probability density function (PDF) of NARX prediction errors. To verify the proposed sensing system and damage identification algorithm, experimental studies considering four rail conditions (i.e., intact, cracked, bump, and welded conditions) are conducted in this study. First, baseline NARX models are priorly trained using ultrasonic signals acquired on an intact rail segment; then, DSFs corresponding to each condition are calculated enabling effective rail damage diagnosis. To optimize the training parameters of NARX models, three different training functions are utilized to select the most appropriate parameters that are sensitive to rail damages. Last, large-scale testing is conducted, further proving the applicability and reliability of the proposed rail damage evaluation approach. Through this study, it is suggested that integrating high-performance sensing technologies with effective data processing tools would maximumly enable efficient and robust non-destructive testing, especially on important infrastructures such as rails.

A modal-based method for blast-induced damage assessment of reinforced concrete columns: Numerical and experimental validation

Reinforced concrete (RC) columns play a crucial role in the overall performance of modern RC frame buildings under blast loading. The RC columns under blast loads might experience various irreversible consequences such as concrete cracks, crushing and spalling, changing the status of both modal properties and load-carrying capacity. In this paper, the blast-induced damage assessment of the RC columns involving the measurement of modal parameters was introduced and validated. Firstly, a modal-based method for blast-induced damage assessment of RC columns was proposed, in which the damage is characterized as the reduction of axial bearing capacity and further expressed as the explicit function of the vibration frequencies and the mass-normalized displacement mode shapes. Then, the finite-element analysis program LS-DYNA was employed to establish the numerical model of RC columns under axial compression loads and lateral blast loads. The residual load-bearing capacities and modal properties of the RC columns were illustrated. By using the numerical results collected in the present study, the validity of the proposed damage assessment method was discussed and proved numerically. Further, field explosion tests and experimental modal tests were conducted, and the results showed that the modal-based damage assessment method exhibited remarkable agreement with the blast damage determined by residual bearing capacities. Based on the numerical and experimental results, the proposed modal-based damage assessment method is applicable for the non-destructive evaluation of blast-induced damage of RC columns.

Nondestructive Evaluation of Long-Term Creep Damage and Its Availability in ASME Grade 91 Steel Welds

Nondestructive Evaluation of Long-Term Creep Damage and Its Availability in ASME Grade 91 Steel Welds

Non-Destructive Damage Evaluation Based on Static Response for Beam-like Structures Considering Shear Deformation

Shear deformation plays an important role in certain structures, and neglecting shear deformation can affect the accuracy of structural response. This paper proposes a non-destructive damage evaluation method that considers shear deformation, based on static response, for identifying corrosion in beam-like structures. The influence of shear deformation on nodal displacement for simply supported beams with different cross-sections was analyzed. The results indicate that even small errors yield inaccurate identification results when neglecting shear deformation. To solve this problem, analytical displacements of the structure were determined based on the Timoshenko beam theory, and the objective function was established. Additionally, the damage identification results were obtained by minimizing the objective function using the interior point method. Several progressively complex examples were used to demonstrate the effectiveness of the proposed method in identifying damage in beam-like structures.

Deep long short-term memory (LSTM) networks for ultrasonic-based distributed damage assessment in concrete

This paper presented a comprehensive study on developing a deep learning approach for ultrasonic-based distributed damage assessment in concrete. In particular, two architectures of long short-term memory (LSTM) networks were proposed: (1) a classification model to evaluate the concrete’s damage stage; (2) a regression model to predict the concrete’s absorbed energy ratio. Two input configurations were considered and compared for both architectures: (1) the input was a single signal; (2) the inputs were four signals from four sides of the specimen. A comprehensive experimental study was designed and conducted on ground granulated blast furnace slag-based geopolymer concrete, providing a total number of 1920 ultrasonic signals from different damage stages. Unsupervised k-means clustering based on dynamic time warping (DTW) was implemented to cluster the ultrasonic response signals from the experimental study into five defined damage stages. The proposed LSTM architectures were successfully trained and validated using the experimental dataset. Moreover, the performance of the LSTM models was evaluated in noisy environments. The proposed LSTM models in this study used the time series of response signals for damage assessment. Therefore, the damage-sensitive features were automatically extracted by the LSTM layers. For comparison, a set of linear and nonlinear ultrasonic features were manually extracted from the response signals as damage-sensitive features, and their sensitivity to damage was investigated. Artificial neural networks were implemented to combine the extracted features and perform the same tasks defined for LSTM models. Comparing the two approaches showed that using the time series of ultrasonic response signals as the input of LSTM models outperforms the idea of using the manually extracted features. This study showed that the presented method is efficient, reliable, and promising for nondestructive evaluation of damage in concrete.

Research on the fatigue magnetization of ferromagnetic materials under low cycle fatigue based on the magneto-mechanical effect

Fatigue damage that caused by low cycle fatigue may lead to many seriously industrial accidents and economic losses, which can be effectively evaluated by magnetic signal based on the magneto-mechanical model. However, the prevailing theoretical model have some limitations (e.g. inaccurate quantification, many unknown coefficients that difficult to obtain, etc.) in describing fatigue magnetization. A new nonlinear magneto-mechanical model based on J-A model proposed in this paper has superior prediction capability for the magnetization under elasto-plastic deformation. In addition, the evolution of the magnetic signal of ferromagnetic materials during the fatigue process is obtained by combining the viscoplastic model and magneto-mechanical model, the fitting equation of magnetic signal and fatigue damage in material fatigue process are proposed, and the determination coefficients are all greater than 0.99. The results of this paper can provide a basis for the electromagnetic nondestructive evaluation of fatigue damage of ferromagnetic materials in engineering.

Nondestructive damage evaluation of composites based on terahertz and X-ray image fusion

Inspection of thin and multi-depth delamination and foreign inclusion defects inside glass fiber reinforced polymer (GFRP) composites is a challenge for traditional non-destructive testing (NDT) techniques. Terahertz (THz) and X-ray computed tomography (CT) are two prevalent NDT methods, which also have their unique advantages and relative weakness individually. Specifically, in our previous study, it is found that THz NDT method has a higher contrast and X-ray CT NDT method has a higher resolution for detecting defects in GFRP composites. In this paper, a new fusion algorithm applied to the THz and X-ray CT NDT imaging data, based on the combination of saliency region analysis (SRA) and wavelet based multi-scale transforms (W-MST) is proposed to detect delamination and inclusion defects of GFRP composites. Moreover, the strategy of weighted least square optimization (WLSO) is adopted to eliminate the effects of unregistered images. An exhaustive number of combinations (36 in total) of the proposed fusion algorithm, which include the selection of fusion rules for saliency map, wavelet decomposition levels and wavelet basis functions, have been quantitively compared using objective evaluation indices such as standard deviation (SD) and spatial frequency (SF) to quantify the improvement of detection effect in terms of contrast and resolution. Among them, there are five optimal combinations applicable to five pairs of different source images, and the averages of the SD and SF indicators of fused images have increased by 126% and 190% compared with the source images, respectively. The inspection efficacy of defects using the proposed fusion method has been analyzed visually, showing that the new approach can effectively accentuate the complementary advantages of THz and X-ray NDT methods, resulting in quantifiably improved defect inspections.

Nondestructive Evaluation of Damage in GFRP Bars Using Ultrasonic Guided Waves

Abstract Glass fiber-reinforced polymer (GFRP) bars are a common alternative to steel reinforcement, offering economical and safety benefits of avoiding/limiting corrosion in traditionally reinforc...

Nonlinear resonant ultrasound spectroscopy (NRUS) for nondestructive evaluation

Nonlinear Resonant Ultrasound Spectroscopy (NRUS) is a simple yet effective technique for nondestructive evaluation of incipient damage in a wide range of materials. NRUS combines the advantages of resonance-based testing and nonlinear ultrasonic or acoustic testing in that it can be used for testing complex-shaped objects and detection of microscopic defects. In this talk, I describe several applications of NRUS in: (1) monitoring of microcracking in concrete materials due to various damage processes; (2) monitoring the growth of microscopic fatigue cracks in aluminum; and (3) predicting the fatigue life of additively manufactured metallic alloys. In addition, I provide a snapshot of our ongoing research in developing non-contact NRUS test set up and using NRUS for in-process monitoring of additively manufactured parts. I close with describing the limitations of NRUS and possible directions for future research to realize NRUS potential in nondestructive evaluation (NDE) applications.

Machine learning aided design of smart, self-sensing fiber-reinforced plastics

Numerous techniques have been developed for the non-destructive evaluation (NDE) of impact damage in fiber reinforced plastics (FRPs), following the increasing demands for their safety and maintenance. Considering the large-scale detection and the vast amount of data involved, machine learning (ML) can be utilized in NDE for damage type analysis and impact damage localization. Furthermore, self-sensing using carbon fiber in FRPs is an emerging technique for NDE that can be combined with ML. In this study, ML was used to design smart FRPs by selecting the fiber type and electrode distance considering the cost and electromechanical sensitivity. Furthermore, a novel algorithm for structural health self-sensing was suggested using an artificial neural network. The developed ML algorithms are advantageous since they do not require a theoretical model when all the factors and the variables of FRPs, such as the maximum absorbed impact energy, maximum impact force, initial electrical resistance, number of electrodes, fiber types, and electrode distance, are to be considered. The algorithm was trained using given input data and the target, and the output could be successfully obtained when new input data were provided. Therefore, the proposed ML algorithms hold great potential and applicability to FRP design and for NDE methods.

Laboratory application of sampling approaches to inverse scattering

This study presents an experimental investigation of the recently established generalized linear sampling method (GLSM) (Pourahmadian et al 2017 Inverse Problems 33 055007) for non-destructive evaluation of damage in elastic materials. To this end, ultrasonic shear waves are generated in a prismatic slab of charcoal granite featuring a discontinuity interface induced by the three-point bending (3PB). The interaction of probing waves with the 3PB-induced damage gives rise to transient velocity responses measured on the sample’s boundary by a 3D scanning laser Doppler vibrometer. Thus obtained waveform data are then carefully processed to retrieve the associated spectra of scattered displacement fields. On deploying multifrequency sensory data, the GLSM indicators are computed and their counterparts associated with the classical linear sampling method (LSM) (Cakoni and Colton 2003 Inverse Problems 19 279–95) for comparative analysis. Verified with in situ observations, the GLSM map successfully exposes the support of hidden scatterers in the specimen with a remarkable clarity and resolution compared to its predecessor LSM. It is further shown that the GLSM remains robust for sparse and partial-aperture data inversion, thanks to its rigorous formulation. For completeness, the one-sided reconstruction by both indicators is investigated.

Ultrasonic and thermographic fatigue monitoring on a full-scale CFRP aeronautical component after repairing

The identification of defects in real components made in composite material assumes considerable importance in the aeronautical field, as irregularities in the material can compromise its functionality. Also, the possibility (NDT) to verify the effectiveness of repairing on the component during exercise through non-destructive testing techniques has great importance.The goal of the present study is the application of the ultrasound and thermographic techniques for the identification of defects in a repaired component and the evaluation of the damage caused by the application of cyclical fatigue loads. All the important factors were studied, improving ultrasound scanning and allowing evaluating the effectiveness of the techniques used. In this paper, one case of study was proposed for non-destructive damage evaluation employing Phased Array ultrasonic and thermographic methods on aeronautical CFRP component. The structural element analysed is a spar with a double-T section with a hole and presents a repair by scarfing and hot bond process. The experimental results obtained shown the validity of the ultrasonic and thermographic technique for the sensitivity in detecting defects on full-scale aeronautical components with short execution and scan times.

Cumulative fatigue damage in thin aluminum films evaluated non-destructively with lasers via zero-group-velocity Lamb modes

In this paper, a non-contact non-destructive evaluation of cumulative fatigue damage in 75μm-thick aluminum plates is conducted by using the first symmetric zero-group-velocity Lamb mode and taking benefit of its local and long-lasting resonance feature. The tested aluminum sheets are subjected to fatigue loading, in a two sides clamped compression configuration inducing buckling. For understanding the experimental observations, we propose and establish an empirically-inspired theoretical modeling based on the cumulative damage theory, completed with a finite element simulation, for comparison with the experimental measurements. The observed phenomena along fatigue cycles show the potential for the prediction of the fatigue lifetime and the quantitative assessment of different stages of the fatigue damage in solid plate structures. Good agreement is found between the proposed theory/simulation and the experiment on zero-group-velocity resonance frequency. The quality factor of zero-group-velocity resonance is also experimentally studied and compared with numerical calculations, a disagreement is observed after ∼30% of fatigue lifetime. This point elucidates the start time of the change of mechanical properties during the early fatigue stage and is identified as a potential path for the improvement of the proposed empirical model in the future.

Thermal damage evaluation in nickel plate by nonlinear electromagnetic acoustic resonance technique

Nickel and nickel-based composites are of vital importance in many fields, while temperature loading can greatly influence the strength and performance of the materials. Nondestructive evaluation and characterization of such thermal damage can be used to predict the failure of metallic structures, thermal barrier coatings and so on, especially in a non-contact way under certain strict circumstances, such as testing at high temperature or in radiative environment. Herein, a contactless ultrasonic technique employing electromagnetic acoustic transducers (EMATs) combined with the resonance ultrasound spectroscopy is applied to make up the low energy transition efficiency of EMATs and enhance the signal-to-noise ratio of ultrasonic testing signals. The method is adopted to assess the thermal damages of different levels in artificially heat loaded nickel plates. The damage sensitivity of third order harmonics generated from shear waves is discussed, along with linear ultrasonic features including wave velocity and attenuation. Experimental results show that the proposed nonlinear electromagnetic acoustic resonance (EMAR) technique can be used to evaluate the thermal damage in ferromagnetic material with improved reliability and sensitivity over linear ones.

Non-destructive damage evaluation of Ni-base heat-resisted alloy in the air by spectrum analysis of the reflected visible light

Non-destructive damage evaluation of Ni-base heat-resisted alloy in the air by spectrum analysis of the reflected visible light

Enhanced composite plate impact damage detection and characterisation using X-Ray refraction and scattering contrast combined with ultrasonic imaging

Ultrasonic imaging and radiography are widely used in the aerospace industry for non-destructive evaluation of damage in fibre-reinforced composites. Novel phase-based X-ray imaging methods use phase effects occurring in inhomogeneous specimens to extract additional information and achieve improved contrast. Edge Illumination employs a coded aperture system to extract refraction and scattering driven signals in addition to conventional absorption. Comparison with ultrasonic immersion C-scan imaging and with a commercial X-ray CT system for impact damage analysis in a small cross-ply carbon fibre-reinforced plate sample was performed to evaluate the potential of this new technique. The retrieved refraction and scattering signals provide complementary information, revealing previously unavailable insight on the damage extent and scale, not observed in the conventional X-ray absorption and ultrasonic imaging, allowing improved damage characterisation.