Research on crack damage localisation and shape visualisation method for aluminum plates based on Lamb waves

Modified reconstruction algorithm for probabilistic inspection of damage based on damaged virtual sensing paths

Application of a Probabilistic Algorithm for Ultrasonic Guided Wave Imaging of Carbon Composites

This study presents a method to map the regions of debonding of a thin composite film attached to a steel plate based on the changes in Lamb wave signatures. The film in discussion is a passive radiative cooling film that can be coated on the top of metallic roof shingles to reflect the sunlight and subsequently cool the building. It is an energyefficient building material that can potentially reduce building energy consumption by up to 30%. Its effectiveness can, however, be decreased by damage or debonding of the film from the substructure. Hence, it is essential to identify the regions of debonding/ damage in a timely manner for rectification. Here, we demonstrate a method to map the regions of debonding based on changes observed in Lamb waves when they pass through a debonded region. When the Lamb waves pass through this film, they get attenuated and arrive with a slight delay. By quantifying the changes in the wavepackets, the debonded regions can be mapped tomographically. With this as the basis, experiments were conducted on a steel plate bonded with the film to demonstrate the mapping of debonded regions. A small section of the film was removed to simulate a damaged scenario. PZT discs were bonded to the structure along the edges of the square to create a dense network of paths passing through the film. Along each path, signals are acquired in pristine and damaged conditions to estimate the damage index for each path. The Reconstruction Algorithm for Probabilistic Inspection of Damage (RAPID) technique was employed to construct the map, where high-intensity regions denoted areas with a higher likelihood of debonding. The obtained map was compared with the structure to validate the method and establish it as a viable option. Through a successful demonstration, this work lays the foundations for identifying and quantifying regions of film debonding.

Corrosion damage presents significant challenges to the safety and reliability of connected vehicles. However, traditional non-destructive methods often fall short when applied to complex automotive structures, such as bolted lap joints. To address this limitation, this study introduces a novel corrosion monitoring approach using Lamb wave-based weighted fusion imaging methods. First, the Minimum Variance Distortionless Response (MVDR) is utilized to process Lamb wave signals collected under bolt-loosening and bolt-tightening conditions to image the bolt locations. Second, based on the identified bolt positions, the weighted Reconstruction Algorithm for Probabilistic Inspection of Damage (RAPID) is applied to the Lamb wave signals acquired before and after corrosion, enabling precise imaging of the actual positions of the corroded bolts. Experiments are conducted on three-bolt lap joints in cases of single-corrosion and two-corrosion using A0 mode Lamb waves and piezoelectric sensor networks. The results demonstrate that the proposed method effectively images multiple types of damage and achieves maximum location deviations of 7.43 mm. This approach enables precise and visual multi-damage assessment, particularly in hard-to-access regions. When integrated with V2X-enabled (Vehicle-to-Everything) systems, the method offers potential for incorporation into automotive structural health monitoring systems for remote diagnosis in complex structures, thereby enhancing monitoring efficiency and accuracy.

Research on spatial localization method of composite damage under strong noise

Lamb wave based structural health monitoring shows a lot of potential for damage detection of composite structures. However, currently there is no agreement upon optimal network arrangement or detection algorithm. The objective of this research is to develop a sparse network that can be expanded to detect damage over a large area. To achieve this, a novel technique based on damage progression history has been developed. This technique gives an amplification factor to data along actuator-sensor paths that show a steady reduction in transmitted power as induced damage progresses and is implemented with the reconstruction algorithm for probabilistic inspection of damage (RAPID) technique. Two damage metrics are used with the algorithm and a comparison is made to the more commonly used signal difference coefficient (SDC) metric. Best case results show that damage is detected within 12 mm. The algorithm is also run on a more sparse network with no damage detection, therefore indicating that the selected arrangement is the most sparse arrangement with this configuration.

Structural Health Monitoring (SHM) of Carbon Fiber Reinforced Polymers (CFRP) has become, recently, in a promising methodology for the field of Non-Destructive Inspection (NDI), specially based on Ultrasonic Guided Waves (UGW), particularly Lamb waves using Piezoelectric Transducers (PZT). However, the Environmental and Operational Conditions (EOC) perform an important role on the physical characteristics of the waves, mainly the temperature. Some of these effects are phase shifting, amplitude changes and time of flight (ToF) variations. In this paper, a compensation method for evaluating and compensating the effects of the temperature is carried out, performing a data-driven methodology to calculate the features from a dataset of typical temperature values obtained from a thermoset matrix pristine plate, with a transducer network attached. In addition, the methodology is tested on the same sample after an impact damage is carried out on it, using RAPID (Reconstruction Algorithm for Probabilistic Inspection of Damage) and its geometrical variant (RAPID-G) to calculate the location of the damage.

Composite materials are widely used in various engineering applications such as aerospace, automotive, construction, and sports equipment due to their superior mechanical properties, including a high strength-to-weight ratio, design flexibility, and resistance to fatigue and corrosion. However, the complex nature of composite materials and their damage mechanisms poses a challenge to effective Structural Health Monitoring (SHM). Guided waves have been shown to be an effective non-destructive testing technique for localizing damage in Carbon Fiber Reinforced Polymer (CFRP) materials when used with the RAPID (Reconstruction Algorithm for Probabilistic Inspection of Damage) algorithm, which has high robustness and efficiency. However, in some cases, the RAPID algorithm may suffer from inaccuracies in predicting damage positions due to the intersection points among transducer paths. To address this issue, this paper proposes a correction to the original RAPID algorithm. The correction introduces weights to the probability of damage position, computed from the number of intersections that cross that position, to mitigate the impact of the intersection problem. Additionally, the Q-statistic, which is calculated through principal components analysis (PCA), is employed as a damage position indicator. To evaluate the effectiveness of the proposed correction, the measurement data from 12 transducers mounted on a 500 × 500 mm CFRP plate with a reversible damage model placed in different positions are used. The results of the proposed method are compared to the results of RAPID, a suggested geometrical correction of the RAPID algorithm. The findings demonstrate that the proposed correction provides an effective means to predict damage positions with greater accuracy than the original RAPID algorithm. Furthermore, the proposed correction does not compromise the computational simplicity of the RAPID algorithm, which is one of its key advantages.

Detecting and localizing damage of aircraft structures using naturally occurring air flow provide a viable means of developing more efficient structural health monitoring systems. Such passive sensing techniques eliminate the need for acquiring power either from the battery or ambients to generate controlled excitation. In a recent study, high-pressure air was randomly sprayed on metallic panels via air blow gun to mimic nonstationary and randomly distributed nature of turbulent flow occurred during operation of the flight. Self-Green’s functions (SGF) were reconstructed using auto-correlation (AC) at 169 (13 × 13) sensing points covering a 150 mm ´ 150 mm area. Mean square error (MSE) of SGFs between damaged and pristine metallic panels was able to map the damage location in the panel. To inspect a larger area with fewer sensing points, a damage imaging technique based on cross-correlation (CC), reconstruction algorithm for probabilistic inspection of damage (RAPID), and image fusion is proposed in this paper. A flat aluminum panel was tested to validate the proposed idea, and an integrally stiffened aluminum panel was studied. Green’s functions (GF) were extracted between sparsely distributed sensor arrays. Eight sensors were attached to each of the aluminum panels to cover an enclosed area (up to 200 mm ´ 200 mm) to record the structural response for the pristine and damaged panel. Compressed air was manually swept the panel in arbitrary directions to create nonstationary and random excitation. GFs were reconstructed using cross-correlation, and RAPID and image fusion were used for generating the image of the damage location. The result indicates that the stability of GFs is the key to accurately pinpoint the damage. At least 20 seconds of long-time recording from sensor guarantees such stability, and well-distributed air impact locations results in symmetric GFs. Damage images are in good agreement with the real damage locations. The proposed technique may detect and localize damage utilizing turbulent flow, and it enlarges the inspection region while significantly reduces the sensing points.

Current damage localisation methods often require many sensors and complex signal processing methods. This paper proposes a fusion algorithm based on elliptical localisation and the reconstruction algorithm for probabilistic inspection of damage (RAPID) to locate and image multiple damages. Experimental verification of the damage algorithm was conducted. An ultrasonic probe was used to excite Lamb signals on an aluminium alloy plate, the ultrasonic response signals at different positions within the plate under multiple damages were measured and the constructed algorithm was employed to image the damage location. In the experiment, this method improved localisation efficiency by excluding invalid sensing paths in the sensor network, saving 31.32% of computational time. When some sensors in the sensor network were damaged, this algorithm ensured a positioning accuracy with a positioning error of 5.83 mm. Finally, the algorithm was used to locate multiple damages in the sensor network and the results showed the good robustness of the algorithm.

Carbon fiber-reinforced polymers (CFRPs) are extensively employed in the aerospace industry due to their excellent properties. Delamination damage occurring at critical locations in CFRPs can seriously reduce the safety of in-service components. The detection and localization of delamination damage using Lamb waves hold significant potential for widespread application in non-destructive testing. However, the choice of damage localization algorithm may produce different delamination damage localization results. This research presented an IRAPID (improved reconstruction algorithm for probabilistic inspection of defects) method derived from the RAPID (reconstruction algorithm for probabilistic inspection of defects) method, aiming to improve the accuracy and reliability of delamination damage localization. Three CFRP curved plates, including a healthy curved plate and two curved plates with delamination damage sizes of Φ20 mm and Φ40 mm, were prepared in the experiment. The detection experiment of the CFRP curved plate using lead zirconate titanate (PZT) as a transducer to excite and receive Lamb waves was conducted, and the influence of excitation signal frequency on the performance of the proposed method was discussed. Under the condition of an excitation signal frequency of 220~320 kHz and a step size of 10 kHz, the accuracy of the delamination damage localization method proposed in this paper was compared with that of existing methods. The experimental results indicate that the IRAPID algorithm exhibits good stability in the localization of delamination damage across the range of frequency variations considered. The localization error of the IRAPID algorithm for delamination damage is significantly lower than that of the DaS (delay-and-sum) algorithm and the RAPID algorithm. As the size of the delamination damage increases, so does the localization error. The accuracy of delamination damage localization is lower in the X-axis direction than in the Y-axis direction. By averaging the localization results across various frequencies, we can mitigate the potential localization errors associated with single-frequency detection to a certain extent. For the localization of delamination damage, Lamb waves at multiple frequencies can be employed for detection, and the detection results at each frequency are averaged to enhance the reliability of localization.

In a recent study, Agrahari and Kapuria (J Int Mater Sys Struct, 27, 1283–1305, 2016) presented a Lamb wave based refined time reversal method (RTRM) for baseline-free damage detection in thin plate structures. In this method, it was proposed to conduct the probe at the frequency of best reconstruction and to use an extended wave packet ranging between two side bands accompanying the main mode of the reconstructed signal for computing the damage index. The method showed excellent sensitivity to damage in a single actuator-sensor path scenario. In the present work, the RTRM is integrated with a damage imaging algorithm called the reconstruction algorithm for probabilistic inspection of defects (RAPID) to develop an accurate baseline-free damage localization technique using a network of piezoelectric wafer patch transducers. Its performance is tested experimentally in an aluminium plate with a block mass damage. It is found that the proposed RTRM conducted at the best reconstruction frequency of the sensor network-plate system is able to predict the damage location with very good accuracy, whereas the other existing baseline-free methods such as the conventional TRM with single mode tuning and the reciprocity principle based method are either ineffective in localizing or give highly erroneous prediction of the damage location.

Enhanced algorithm for damage location on composite material

The aviation industry faces the challenge of offering aircraft that are lighter, more economical, and safer. One of the solutions is to increase the use of composites. For these materials, adhesive bonding has proven to be the appropriate joining technology. To check these adhesive joints, costly and time-consuming maintenance measures are carried out. An intelligent Structural Health Monitoring (SHM) system can extend these intervals and allow the use of a predictive maintenance system. This paper describes the method of Ultrasonic Lamb Waves for monitoring a adhesively bonded Carbon Fiber Reinforced Polymer (CFRP) aircraft fuselage. Prior to this, the production of a segment of a fuselage and the characterization of the materials (CFRP and adhesive) is shown. Afterwards the method of Ultrasonic Lamb Waves with the use of piezoelectric transducers and signal processing based on the Reconstruction Algorithm for Probabilistic Inspection of Damage (RAPID) algorithm are explained. At the end, the experimental evaluation of an undamaged and a damaged fuselage structure is done. The results have shown the possibility of RAPID algorithm for damage detection on adhesive. An outlook on future work is given.

Ultrasonic methods for damage imaging typically use baseline signals from the undamaged part, which are often affected by real operational conditions and may not always be available. This paper proposes a baseline-free damage imaging algorithm based on a combination of virtual time reversal (VTR) and the Reconstruction Algorithm for Probabilistic Inspection of Damage (RAPID) tomographic technique. VTR is an alternative to traditional time reversal as it reduces the burden of physically back-propagating re-mitted signals by applying signal operations between the transmitted and received waveforms. Spatial VTR-based damage indices were here proposed to enhance defect detection as they do not require the time domain reconstruction of re-emitted signals. Experimental results on damaged aluminium and composite specimens showed that the proposed VTR-based damage indices coupled to the RAPID imaging algorithm were able to localise material flaws with a maximum localisation error of ~6 mm and ~2 mm for aluminium and composite samples, respectively.KeywordsUltrasoundBaseline-freeVirtual time reversalSignal processingDamage imaging