Ultrasonic Guided Waves Research Articles

High-Precision Corrosion Detection via SH1 Guided Wave Based on Full Waveform Inversion.

Corrosion detection for industrial settings is crucial for safe and efficient operations. Due to its high imaging resolution, the guided-wave full-waveform inversion tomography technique has significant potential for corrosion detection of plate metals. Limited by the long wavelengths of A0 and S0 mode waves, this method exhibits inadequate detection resolution for the earlier shallow and small corrosion defects. Based on the relatively short wavelength characteristics of the SH1 mode wave, we propose a high-precision corrosion detection method via SH1 guided wave using the full waveform inversion algorithms. By conducting finite element simulations of ultrasonic-guided waves on aluminum plates with varying corrosion defects, a comparison was made to assess the detection precision across A0, S0, and SH1 modes. The comparison results showed that, whether for regular or irregular defects, the SH1 mode wave always exhibited higher imaging accuracy than the A0 and S0 mode waves for shallow and small-sized defects. The corresponding experiments were conducted on an aluminum plate with simple or complex defects. The results of the experiments reconfirmed that the full waveform inversion method using SH1 guided wave can effectively reconstruct the shape and size of small and shallow corrosion defects within aluminum plates.

Laplace Domain Boundary Element Method for Structural Health Monitoring of Poly-Crystalline Materials at Micro-Scale

This paper describes, for the first time, the application of an Elastodynamic Boundary Element Method (BEM) in Laplace Domain for the Structural Health Monitoring (SHM) of poly-crystalline materials. The study focuses on Ultrasonic Guided Wave (UGW) propagation and investigates the wave–material interactions at micro-scale. The study aims to investigate the interaction of UGWs with assessing micro-structural features such as grain size, morphology, degradation, and flaws. Numerical simulations of the most common micro-structural features demonstrate the accuracy and validity of the proposed method. Particular attention is paid to the study of porosity and its influence on material macro-properties. Different crystal morphologies such as cubic, rhombic, and truncated octahedral are considered. The detection of voids based on the changes in the amplitude and Time of Arrival (ToA) of the backscattered signal is investigated. The study also considers inter-granular cracks, which cause laceration, and examines flaw position/orientation, length, and distance from a specific reference. Furthermore, a framework is proposed for generating Probability of Detection (PoD) curves using numerical simulations. Experimental tests in pristine conditions are shown to be in good agreement with the numerical simulations in terms of ToA, signal amplitude, and wave velocity. The numerical simulations provide insights into wave propagation and wave–material interactions, including different types of defects at the micro-scale. Overall, the BEM and UGW methods are shown to be effective tools for better understanding micro-structural features and their influence on the macro-structural properties of poly-crystalline materials.

Three-dimensional fatigue crack quantification using densely connected convolutional network-assisted ultrasonic guided waves

Linear and nonlinear ultrasonic guided wave (UGW)-based methods are available for fatigue crack detection and quantification. However, linear methods usually fail to detect undersized cracks, while nonlinear methods struggle to assess cracks once they have extended to a certain degree. The fusion of linear and nonlinear UGW features offers a new opportunity to enhance evaluation precision and effectiveness, yet it necessitates highly complex modeling. Motivated by this, a regression model is proposed based on the famed densely connected convolutional network, DenseNet, for three-dimensional (3D) fatigue crack quantification in both crack initiation and growth stages. Via the continuous wavelet transform (CWT), the spectra of UGW signals embracing both linear and nonlinear features of UGW are obtained. Subsequently, DenseNet is adopted to extract implicit features of spectra images. Finally, the last fully connected layer of DenseNet is modified as a regression layer to estimate the length and depth of 3D fatigue cracks. A dataset comprising 500 UGW signals is created for validating the proposed model. The results demonstrate that the model can characterize the length and depth of 3D cracks in both initiation and growth stages, establishing it as a promising proof-of-concept for the deep learning-based quantification of 3D fatigue cracks.

Deep Learning Enriched Automation in Damage Detection for Sustainable Operation in Pipelines with Welding Defects under Varying Embedment Conditions

Welded joints in metallic pipelines and other structures are used to connect metallic structures. Welding defects, such as cracks and lack of fusion, are vulnerable to initiating early-age cracking and corrosion. The present damage identification techniques use ultrasonic-guided wave procedures, which depend on the change in the physical characteristics of waveforms as they propagate to determine damage states. However, the complexity of geometry and material discontinuity (e.g., the roughness of a weldment with or without defects) could lead to complicated wave reflection and scatters, thus increasing the difficulty in the signal processing. Artificial intelligence and machine learning exhibit their capability for data fusion, including processing signals originally from ultrasonic-guided waves. This study aims to utilize deep learning approaches, including a convolutional neural network (CNN), Long-short term memory network (LSTM), or hybrid CNN-LSTM model, to demonstrate the capability in automation for damage detection for pipes with welded joints embedded in soil. The damage features in terms of welding defect types and severity as well as multiple defects are used to understand the effectiveness of the hybrid CNN-LSTM model, which is further compared to the two commonly used deep learning approaches, CNN and LSTM. The results showed the hybrid CNN-LSTM model has much higher classification accuracy for damage states under all scenarios in comparison with the CNN and LSTM models. Furthermore, the impacts of the pipelines embedded in different types of materials, ranging from loose sand to stiff soil, on signal processing and data classification were further calibrated. The results demonstrated these deep learning approaches can still perform well to detect various pipeline damage under varying embedment conditions. However, the results demonstrate when concrete is used as an embedding material, high attention to absorbing the signal energy of concrete could pose a challenge for the signal processing, particularly under high noise levels.

Elastic Wave Mechanics in Damaged Metallic Plates

Human health monitoring (HHM) is essential for continued daily task execution, as is structural health monitoring (SHM) for structures to ensure the continual performance of their designed tasks with optimal efficiency. The existence of damage in a structure affects its optimal use through stiffness deterioration. Damage of different forms could occur in a structure but have the singular objective of material degradation, leading to its underuse for a task. Guided wave ultrasonics has shown strength in detecting sundry damage in structures, but most of the damage monitored and detected is unfilled with substances. However, some damage could trap and accumulate substances that could hasten material degradation through corrosion activities under favorable conditions, especially in the oil and gas industry. This study used the ultrasonic-guided waves’ pitch–catch inspection technique to identify damage filled with different materials. The assessment was based on the RMSD of the dominant Lamb wave mode’s average maximum amplitude and the response signals’ transmission coefficient (TC). A five-cycle tone burst of excitation signals of different frequencies was created to generate propagating Lamb waves in the structure. The fundamental antisymmetric mode was found to be more sensitive than the fundamental symmetric mode when detecting damage filled with various substances. At 80 kHz, the deviation of the current response signals from the baseline response signals due to different filled substances in the damage was distinct and decreased with increased fluid viscosity. Given that structures in the oil and gas sector are particularly susceptible to substance-induced damage, the outcomes of this study are paramount.

Reconstruction of Water-Filled Pipe Ultrasonic Guided Wave Signals in the Distance Domain by Orthogonal Matching Pursuit Based on Dispersion and Multi-Mode.

Ultrasonic guided waves (UGWs) in water-filled pipes are subject to more severe dispersion and attenuation than vacant pipes, posing significant challenges for defect identification and localization. To this end, a novel sparse signal decomposition method called orthogonal matching pursuit based on dispersion and multi-mode (DMOMP) was proposed, which utilizes the second-order asymptotic solution of dispersion curves and the conversion characteristics of asymmetric UGWs in the defect contact stage to reconstruct the dispersive signals and converts the time-domain dispersive signals to distance-domain non-dispersive signals by dispersion compensated time-distance mapping. The synthesized simulation results indicate that DMOMP not only exhibits higher reconstruction accuracy compared to OMP, but also reveals more accurate and stable mode recognition and localization compared to DOMP, which only considers the dispersion under perturbation and noise. In addition, the UGW testing experimental results of water-filled pipes verify the effectiveness of DMOMP, the localization accuracies of three feature signals (defct 1, defct 2 and end echo) with DMOMP are 99.10%, 98.72% and 98.36%, respectively, and the average localization accuracy of DMOMP is as high as 98.73%.

A data-driven hybrid approach to generate synthetic data for unavailable damage scenarios in welded rails for ultrasonic guided wave monitoring

Developing reliable ultrasonic-guided wave monitoring systems requires a significant amount of inspection data for each application scenario. Experimental investigations are fundamental but require a long period and are costly, especially for real-life testing. This is exacerbated by a lack of experimental data that includes damage. In some guided wave applications, such as pipelines, it is possible to introduce artificial damage and perform lab experiments on the test structure. However, in rail track applications, laboratory experiments are either not possible or meaningful. The generation of synthetic data using modelling capabilities thus becomes increasingly important. This paper presents a variational autoencoder (VAE)-based deep learning approach for generating synthetic ultrasonic inspection data for welded railway tracks. The primary aim is to use a VAE model to generate synthetic data containing damage signatures at specified positions along the length of a rail track. The VAE is trained to encode an input damage-free baseline signal and decode to reconstruct an inspection signal with damage by adding a damage signature on either side of the transducer by specifying the distance to the damage signature as an additional variable in the latent space. The training data was produced from a physics-based model that computes virtual experimental response signals using the semi-analytical finite element and the traditional finite element procedures. The VAE reconstructed response signals containing damage signatures were almost identical to the original target signals simulated using the physics-based model. The VAE was able to capture the complex features in the signals resulting from the interaction of multiple propagating modes in a multi-discontinuous waveguide. The VAE model successfully generated synthetic inspection data by fusing reflections from welds with the reflection from a crack model at specified distances from the transducer on either the right or left side. In some cases, the VAE did not exactly reconstruct the peak amplitude of the reflections. This study demonstrated the potential and highlighted the benefit of using a VAE to generate synthetic data with damage signatures as opposed to using superposition to fuse the damage-free responses containing reflections from welds with a damage signature. The results show that it is possible to generate realistic inspection data for unavailable damage scenarios.

Nondestructive Testing and Health Monitoring Techniques for Structural Effective Prestress

Prestressed structures are widely employed in bridges and large-span spatial structures, and the accurate evaluation of prestress state is of great importance for structural maintenance. This paper reviews the nondestructive testing (NDT) and health monitoring techniques for structural effective prestress. Specifically, the fiber Bragg grating (FBG) sensor-based, magnetic-elastic (ME) sensor-based, dynamic response-based, ultrasonic guided wave (UGW)-based, electromechanical impedance (EMI)-based, and electrical resistance-based methods are reviewed in this paper. Firstly, the principle, application range, and measuring accuracy of each technique are introduced and analyzed, and the benefits and limitations of each technique are summarized: The FBG sensor and ME sensor take on high measuring accuracy and have been applied in practical engineering, but they are required to be preinstalled during structural construction; the dynamic response-based method is greatly effective in cable force assessment but not suitable for prestress evaluation of prestressed concrete (PSC) structures; the UGW-based, EMI-based, and electrical resistance-based methods have shown favorable potential for prestress assessment in laboratory experiments, but their feasibility and accuracy in practical engineering need to be verified. Secondly, the challenges and discussion of each method are discussed in the following four aspects: measuring range, reliability of measuring results, stability and durability considering long-term monitoring, and cost-efficiency. Finally, a decision tree is proposed to choose the most appropriate prestress evaluation method in a specific application scenario.

Influence of Composite Thickness on Ultrasonic Guided Wave Propagation for Damage Detection Using Embedded PZT Transducers

This paper describes a study that focuses on assessing the influence of composites with different thicknesses (2 mm, 4 mm, and 9 mm) on embedded ultrasonic guided waves (UGWs) under varying temperatures. The study also demonstrates the effectiveness of these embedded sensors in identifying damage. A novel cut-out method that included an embedded diagnostic layer and phased-array lead zirconate titanate (PZT) transducers, created using the ink-jet printing technique in the manufacturing process was employed. The research then focused on studying the behavior of UGWs under varying temperatures for each composite panel. This analysis aimed to understand how temperature variations affected the propagation of guided waves in thick composites. Finally, artificial damage on the surface and impact damage were introduced, both embedded and surface-mounted PZT transducers were used to detect and locate these damages in different thickness composite panels. The results of damage localization indicated that the embedded PZT transducers were more sensitive than the surface-mounted transducers in locating the damage in thick composites.

Rail fracture monitoring based on ultrasonic-guided wave technology with multivariate coded excitation

Steel lay the basis of railroad train traffic. Rail fracture is the most serious injury to the rail, which should be monitored in time. The conventional mono-pulse exciting ultrasonic guide wave (UGW) has low energy, the conventional Barker code has limited coding sequence length and the orthogonal complementary Golay code has the problem of low monitoring efficiency. This study proposes multivariate coded excitation (MCE) to excite UGW to monitor rail fracture, and the feasibility of coding and decoding the method is theoretically derived and verified. Ideally, the MCE generated based on the 3-bit Barker code (B3: 1,1, −1) and 4-bit orthogonal complementary Golay code (GA4: 1,1,1, −1; GB4: 1,1, −1,1) is calculated to have a main-lobe power level (MPL) gain of 8.1020 dB, which is significantly higher than the MPL of Barker and Golay codes. For the proposed method, finite element modeling simulation and experimental study are carried out respectively. Analyze and process the data, and calculate the gain of the difference in amplitude (DIA) between the amplitude of echo caused by rail fracture and the amplitude in the healthy rail. The gain of the DIA of the echoes in the MCE is above 50 dB (experimental data, the value of simulation data is 5 dB) under different degrees of rail fracture, while the gain of the DIA of the echoes caused by the other three excitation methods is below 40 dB (experimental data, the value of simulation data is 1 dB). Simulation and experimental results show that the MCE makes up for the shortcomings of the conventional Barker code and Golay code, improves the excitation energy of the monitoring system, and the high gain of the DIA of the echoes is more conducive to the identification of rail fracture damage.

Composite Panel Damage Classification Based on Guided Waves and Machine Learning: An Experimental Approach

Ultrasonic guided waves (UGW) are widely used in structural health monitoring (SHM) systems due to the sensitivity of their propagation mechanisms to local material changes, i.e., those induced by damage. Post-processing of the signals gathered by piezoelectric sensors, typically used for both the excitation and the sensing of UGW, is a fundamental step to extract all the peculiar features that can be related to both damage location and severity. This research probes the efficacy of machine learning (ML) models in discerning damage location (R-Classification) and size (S-Classification). Seven supervised ML classifiers were examined: Ensemble-Subspace K-Nearest Neighbors (KNN), Ensemble-Bagged Trees, KNN-Fine, Ensemble-Boosted Trees, Support Vector Machine (SVM), Linear Discriminant, and SVM-Quadratic. The experimental dataset comprised measurements from varied reversible damage configurations on a composite panel, represented by wooden cuboids of single and three different sizes. Signal noise was minimized by performing a low-pass filter, and sequence forward selection-aided feature selection. The optimized ensemble classifier proved to be the most precise for R-Classification (95.83% accuracy), while Ensemble-Subspace KNN excelled in S-Classification (98.1% accuracy). This method offers accurate, efficient damage diagnosis and classification in composite structures, promising potential applications in aerospace, automotive, and civil engineering sectors.

Damage localization in carbon fiber composite plate combining ultrasonic guided wave instantaneous energy characteristics and probabilistic imaging method

The ultrasonic guided wave (UGW) imaging technique has become a promising tool in composite structure health monitoring. However, most algorithms extract damage characteristics with time or frequency domain method. These methods cannot characterize the nonlinear and nonstationary damage signals. Based on the Hilbert–Huang transform, the phenomenon of instantaneous energy increase and decrease is studied, and the rule of energy change and distance from damage to sensor path is summarized. The damage index based on instantaneous energy characteristics is proposed. A probabilistic imaging method is proposed, which introduces a probability distribution function based on the elliptical method for fusion imaging with traditional probability distribution function. The experiment of 74 points on carbon fiber composite plate was performed. The average localization error was 3.56 mm and the maximum was 9.68 mm. The proposed method has high localization accuracy and stability and has application prospects in high-end equipment composite structure health monitoring.

Investigation of pipelines defect localization for fusion reactor by using T(0,1) mode ultrasonic guided waves

The stable operation of a fusion reactor depends on the reliable functioning of various accessory systems, many of which comprise extensive pipelines with different diameters and diverse cladding. Defects in pipelines can occur due to manufacture, thermal stress, and fatigue, threatening the systems’ safety and reliability. Ultrasonic guided waves (UGWs) testing offers a promising approach for remotely locating these minute defects. The study explores non-dispersive fundamental torsional guided (T(0,1)) waves, particularly their influence on minor defect detection in stainless steel pipes. Both finite element simulations and experimental trials reveal that reflection coefficients, indicative of defect presence, rapidly escalate from 64 to 128 kHz. The optimal frequency for minor defect detection lies between 128 and 180 kHz. A significant finding from the circumferential distribution spectrum and angle profile reveals pipe diameter's dominant influence on the reflection energy distribution concentration. Furthermore, this highlights the unique ability of T(0,1) guided waves to precisely locate defects in the circumferential direction within large-diameter pipes over long distances. The results underscore the promising potential of UGWs testing in complex pipelines of fusion reactors.

Defect detection and identification for aircraft cable insulation layer based on deep forest

Due to the special internal environment of aircraft, cable damage is inevitable, which usually starts from insulation layer defects and may cause major economic losses, and even seriously threaten the life of people on board. According to the above issue, a defect detection system for aircraft cable insulation layer based on ultrasonic guided waves (UGWs) is built in this paper. In order to ensure the complete coupling between the sensor and the insulation layer of the aircraft cable, the macro fiber composite (MFC) flexible sensor is employed in the system. Both simulation and experimental results show that this method can simultaneously monitor four different types (abrasion, cut, semi-stripping and full-stripping) of cable insulation layer defects at different locations on the same cable. The reflection signals of different types of defects are extremely similar and difficult to distinguish directly. In this paper, a classification method for four types of defects based on the deep forest method is proposed. This method requires a small sample size, and the classification performance is not affected by network structure and parameters. The recognition accuracy can reach 100%, avoiding the problem of traditional deep learning classification relying on a large number of samples and requiring parameter adjustment. The proposed method in this paper is proven to be able to effectively carry out online monitoring and accurately classify defect types, which has guiding significance for aircraft maintenance personnel to take corresponding measures in time.

Ultrasonic guided wave imaging of pipelines based on physics embedded inversion neural network

Pipeline corrosion quantification plays a vital role in guaranteeing the safety of critical industrial structures and thus significant work has been carried out to address such an issue. Although quantitative imaging is crucial for non-destructive testing, research in guided wave pipeline testing has primarily centered on qualitative approaches. Here, we propose a deep neural network built upon physical model to reconstruct pipe wall thickness from ultrasonic guided wave (UGW) signals. The workflow of reconstruction contains three layers, where each layer consists of a fixed forward network and a residual inversion network. The forward model is represented by an agent convolutional neural network which would be embedded into the entire inversion network. The residuals between data from the forward model and real signals are then mapped into velocity profile differences through sub-inversion network. Numerical experiments were conducted to verify the inversion performance of the deep neural network using thickness maps obtained from guided wave frequency domain information. Results show that inversion images are capable to reveal the positions, shapes, and depths of corrosion with high resolution and precision, yielding an average inversion of 87.37% in the test set. In addition, by utilizing the periodicity of the pipeline, the inversion accuracy of eight pairs of transducers were improved from 67.7% to 89.43% with high-order helical guided wave. Compared with traditional high-precision inversion methods such as full waveform inversion, the proposed method achieved approximately 300 times faster inversion speed at the cost of some accuracy. The research demonstrates that real-time quantitative imaging of defects on pipes can be achieved accurately by physics embedded network. Furthermore, an experimental verification of the method was carried out through UGW pipeline testing, demonstrating its feasibility. The mean squared error of wall thickness reconstruction was 0.0070, achieving a high level of precision.

A New Method for Erosion Prediction of 90° Elbow Based on Non-Axisymmetric Ultrasonic-Guided Wave and the PSO-LSSVM Algorithm.

The non-axisymmetric exciting guided wave can detect the thinning section of the elbow, and the time domain energy value of the signal collected at the outer arch position of the receiving end displays a downward trend as the remaining thickness of the erosion area decreases. To address the difficulty in detecting the erosion degree of the elbow with high accuracy, this paper uses the linear frequency modulation (LFM) signal to excite a non-axisymmetric guided wave that propagates in the 90° elbow and collects signals through four PZT receivers. To predict the erosion degree, the corresponding relationship between the energy value of the four signals after fractional Fourier filtering and the degree of elbow erosion is established through the particle swarm optimization (PSO)-least squares support vector machine (LSSVM) algorithm. The results show that the method proposed has an average accuracy rate of 98.1864%, 94.7167%, 99.119%, and 99.9593% for predicting the erosion degree of four elbow samples, and 94.0039%. and 81.2976% for two new erosion degrees, which are higher than the nonlinear regression model, LSSVM algorithm, and BP neural network algorithm. This study has guiding significance for real-time monitoring of elbow erosion.

Development and validation of a probabilistic multistage algorithm for damage localization in piezo-monitored structures

All structures during operating life can be affected by faults induced by accidental events and operational conditions. Structural health monitoring systems can provide quasi-real-time diagnosis of the structure, thus enabling the condition-based maintenance approach. By means of piezoelectric transducers (PZTs) and ultrasonic guided waves (UGW), the structural integrity can be easily interrogated, even though laborious post-processing techniques are required to correctly interpret sensed data. This work aims to devise a new automatic diagnosis framework based on the propagation of UGW for thin-walled structures fault detection and localisation. Specifically, a fully automated damage identification algorithm was developed through a numerical dataset obtained by finite element simulations, and then validated experimentally. The case of study consisted of a square-shaped aluminium plate equipped with a five PZTs network. Five different damage positions and three different damage sizes were considered. The originality of the proposed algorithm lies in the data processing methodology as well as in its capability to detect damages located inside and outside the sensors network, even close to the panel edges. Algorithm provides, in less than 15 s, indications on the possible damage location and related probability position with a reduced dispersion with respect to other algorithms proposed in literature. A clear image is created displaying the damage position map. The visualisation of the damage position map on the surface of the monitored part allows successful damage imaging and would enable operators to address more efficiently the inspection procedures only in the highlighted areas, reducing maintenance and repair expenses.

Experimental determination of dispersion diagrams over large frequency ranges for guided ultrasonic waves in fiber metal laminates

Fiber metal laminates (FMLs) are of high interest for lightweight structures as they combine the advantageous material properties of metals and fiber-reinforced polymers (FRPs). However, low-velocity impacts can lead to complex internal damage. Therefore, structural health monitoring with guided ultrasonic waves (GUWs) is a methodology to identify such damage. Numerical simulations form the basis for corresponding investigations, but experimental validation of dispersion diagrams over a wide frequency range is hardly found in the literature. In this work the dispersive relation of GUWs is experimentally determined for an FML made of carbon FRP and steel. For this purpose, multi-frequency excitation signals are used to generate GUWs and the resulting wave field is measured via laser scanning vibrometry. The data are processed by means of a non-uniform discrete 2d Fourier transform and analyzed in the frequency-wavenumber domain. The experimental data are in excellent agreement with data from a numerical solution of the analytical framework. In conclusion, this work presents a highly automatable method to experimentally determine dispersion diagrams of GUWs in FML over large frequency ranges with high accuracy.

Quasi-Distributed Fiber Sensor-Based Approach for Pipeline Health Monitoring: Generating and Analyzing Physics-Based Simulation Datasets for Classification

This study presents a framework for detecting mechanical damage in pipelines, focusing on generating simulated data and sampling to emulate distributed acoustic sensing (DAS) system responses. The workflow transforms simulated ultrasonic guided wave (UGW) responses into DAS or quasi-DAS system responses to create a physically robust dataset for pipeline event classification, including welds, clips, and corrosion defects. This investigation examines the effects of sensing systems and noise on classification performance, emphasizing the importance of selecting the appropriate sensing system for a specific application. The framework shows the robustness of different sensor number deployments to experimentally relevant noise levels, demonstrating its applicability in real-world scenarios where noise is present. Overall, this study contributes to the development of a more reliable and effective method for detecting mechanical damage to pipelines by emphasizing the generation and utilization of simulated DAS system responses for pipeline classification efforts. The results on the effects of sensing systems and noise on classification performance further enhance the robustness and reliability of the framework.

A two‐dimensional analysis of the influence of delamination on the wave propagation in a fiber metal laminate

AbstractStructural Health Monitoring (SHM) of composite materials using Guided Ultrasonic Wave (GUW) represent a central research topic with a large range of application fields. One major concern in this context is the detection of hidden inter‐laminar damage such as delaminations in composites, as these cannot be detected by visual inspection [1]. Fiber Metal Laminate (FML) consist of different layers of metal (e.g. steel) and Fiber Reinforced Polymer (FRP) stacked on top of each other and belong to a new type of material system that has been insufficiently explored in the context of damage detection using GUW. Following the objective of damage detection using GUW, two dimensional numerical simulations of the wave propagation in FML composed of layers made from steel and Carbon Fiber Reinforced Polymer (CFRP) are conducted. In order to properly identify and describe the different phenomena occurring when the GUW encounter a delamination three kind of numerical models were considered: undamaged plate, damaged plate with symmetrically and off‐center placed delamination. As far as the modeling of delaminations, the present work is restricted to a zero‐thickness‐delamination generated by de‐merging nodes at the interface of the layers. Mode‐selective excitation (fundamental symmetric and anti‐symmetric mode wave) of the models was then applied and the B‐scan representations of the in‐plane and out‐of‐plane displacement fields were generated and compared. Effects such as reflection, transmission and mode conversion of the main wave are observed and analyzed. Thus, a singular phenomenon as the trapped mode wave can be clearly observed and interpreted.