Lightning strikes pose a significant challenge for aircraft and wind turbine blades with Carbon Fiber Reinforced Polymer (CFRP) structures, requiring reliable damage detection techniques. ...Non-destructive evaluation (NDE) methods, including X-ray and Ultrasonic Testing, are effective in identifying material damage in aircraft. However, X-ray requires access to both sides of the structure, and UT requires a coupling medium between the transducer and the structure, as well as a relatively smooth surface, making both methods less feasible for routine aircraft maintenance. Other NDE techniques, such as eddy current testing and infrared thermography, can detect damage on the side struck by lightning but lack the precision needed for a comprehensive assessment. To address these challenges, this paper introduces a two-stage Fusion-Translation network (FTnet), which integrates NDE 4.0 innovations, including data fusion and advanced imaging algorithms, to optimize the NDE process. By integrating infrared and eddy current data, FTnet characterizes lightning-induced damage with enhanced depth and contour detail, demonstrating superior performance over existing methods in both qualitative and quantitative evaluations. The implementation of FTnet marks an advancement in NDE 4.0, potentially enhancing aircraft safety and streamline maintenance protocols by providing a more reliable and comprehensive assessment of lightning strike damage.
Effective testing of defects in various materials is an important guarantee to ensure its safety performance. Compared with traditional non-destructive testing (NDT) methods, infrared thermography is ...a new NDT technique which has developed rapidly in recent years. Its core technologies include thermal excitation and infrared image processing. In this paper, several main infrared thermography nondestructive testing techniques are reviewed. Through the analysis and comparison of the detection principle, technical characteristics and data processing methods of these testing methods, the development of the infrared thermography nondestructive testing technique is presented. Moreover, the application and development trend are summarized.
Aerospace welds are non-destructively evaluated (NDE) during manufacturing to identify defective parts that may pose structural risks, often using digital radiography. The analysis of these digital ...radiographs is time consuming and costly. Attempts to automate the analysis using conventional computer vision methods or shallow machine learning have not, thus far, provided performance equivalent to human inspectors due to the high reliability requirements and low contrast to noise ratio of the defects. Modern approaches based on deep learning have made considerable progress towards reliable automated analysis. However, limited data sets render current machine learning solutions insufficient for industrial use. Moreover, industrial acceptance would require performance demonstration using standard metrics in non-destructive evaluation, such as probability of detection (POD), which are not commonly used in previous studies. In this study, data augmentation with virtual flaws was used to overcome data scarcity, and compared with conventional data augmentation. A semantic segmentation network was trained to find defects from computed radiography data of aerospace welds. Standard evaluation metrics in non-destructive testing were adopted for the comparison. Finally, the network was deployed as an inspector’s aid in a realistic environment to predict flaws from production radiographs. The network achieved high detection reliability and defect sizing performance, and an acceptable false call rate. Virtual flaw augmentation was found to significantly improve performance, especially for limited data set sizes, and for underrepresented flaw types even at large data sets. The deployed prototype was found to be easy to use indicating readiness for industry adoption.
This study proposes a model that relates the carbon fiber arrangement and orientation in CFRP to the dark-field images captured by X-ray Talbot-Lau interferometer (TLI). X-ray TLI visualizes X-ray ...phase contrast, which is more sensitive to light elements such as carbon, than conventional X-ray absorption contrast. By using TLI, novel non-destructive inspection (NDI) technique for CFRP reinforced by distributed discontinuous carbon fibers, which precisely evaluates local orientation and distribution of fibers in large area, can be developed. The model proposed in this study forms the basis of such NDI technique. In this paper, the model is proposed first, and material parameters needed for the model are then identified from the TLI dark-field images of the multi-fiber composite (MFC) specimens. The proposed model is validated by comparing the dark-field images simulated by the model to those experimentally measured by TLI.
Radiographic non-destructive evaluation (NDE) is an essential technique for understanding defects in welds. These radiographs require certified workers to interpret them to identify the presence of ...defects. Recent deep learning techniques, primarily semantic segmentation, could help welding defect detection and classification. Using image segmentation technology to obtain performance evaluations of the presence, location, and size of defects can improve the stability of defect evaluations while saving a great deal of time. However, supervised instance segmentation requires many manually implemented pixel-level annotations, dramatically increasing the difficulty and cost of achieving non-destructive evaluations. In our work, the weakly supervised semantic segmentation based on the Cut-Cascade RCNN model is used to classify defects. The cascade RCNN obtains the region of interest (ROI) and classification information. In the ROI, adaptive threshold segmentation of the defects is implemented, and the image is filtered to obtain the mask information. The accuracy of using the Cut-Cascade RCNN model in our x-ray dataset size can reach 90.15%.
Accurate detection and localization of moisture damage in asphalt pavements using Ground Penetrating Radars (GPR) has been attracting more and more interest in research. Existing approaches rely ...heavily on human efforts and expert experience and are thus both time and cost consuming and are also subject to accuracy issues caused by stochastic human errors. To address this issue, this paper presents an automated moisture damage detection and localization method by leveraging the state-of-the-art deep learning approach and newly proposed incremental random sampling (IRS) approach. First, 2.3 GHz Ground coupled GPR system was used to survey moisture damages on 16 asphalt pavement bridges to create three moisture damage datasets with different resolutions including 2135 moisture damages and 474 steel joints. On this basis, we propose mixed deep convolutional neural networks (CNN) including ResNet50 network, for feature extraction, and YOLO v2 network, for recognition, to detect and localize moisture damages. In addition, to prepare the input for the deep learning models, an IRS algorithm is proposed to generate suitable GPR images from GPR data to feed the CNN. Comprehensive experimental testing, analysis, and comparison of the proposed approaches are conducted. Experimental results demonstrated the promising performance and superiority of the proposed approaches in detecting and localizing moisture damages in asphalt pavements.
•Moisture damage in bridge deck asphalt pavement is successfully detected and visualized in GPR image.•Moisture damage dataset is constructed from 16 asphalt pavement bridges.•Proposed CNN model is applied to detect moisture damage with F1 score (91.97%), Recall (94.53%) and Precision (91.00%).•A novel IRS algorithm for selecting GPR image with suitable plot scale for deep learning is proposed.•Experimental results demonstrate promising performance in detection and localization of moisture damages with IRS and CNN.
The widespread additive manufacturing technology for producing components having complex geometries has increased the attention towards new reinforced materials, whose internal structure can be ...designed ad hoc on the required mechanical performance. Moreover, fused deposition modelling (FDM) technology allows the building of continuous fibre-reinforced thermoplastic composites. Due to this innovative technology, there is a lack of knowledge mainly about the impact behaviour of these materials, also considering that the main demand comes from the transportation field (automotive, aircraft, etc.), where the impact event is always possible.
Thus, the aim of this research is the investigation of the impact behaviour of specimens obtained by continuous fiber fabrication technique, where Onyx and nylon white filaments were used as matrices and glass fibres for reinforcement. Onyx is a nylon mixed with short carbon fibres. An extensive non-destructive evaluation was performed on the specimens subjected to impact tests to assess the impact damage tolerance of these materials and to measure the damage extension as well. Nevertheless, considering that different non-destructive techniques were employed, the research aims also to suggest the most suitable and reliable technique to detect damage, mainly by on-site inspection.
Corrosion degradation mechanics play a significant role in the safe operability and availability of aircraft components. To defend against corrosion, coating systems and insulation are used on ...aircraft structures to protect them from corrosion promoters. Due to this layer of protection, corrosion under insulation (CUI) and early onset corrosion remain difficult to characterize on exterior aircraft structures in a timely manner. Pulsed eddy current thermography (PECT) shows promise in enabling the detection of corrosion on aircraft while rapidly scanning large areas. However, the limitations of this technique and appropriate measurement parameters have yet to be explored on painted aluminum aircraft structures. In this work, pulsed phase thermography using electromagnetic coil excitation was performed on painted corroded aluminum samples to capture thermal transient localized responses for defect characterization. Defects ranging from 0.4 mm to 5 mm in a painted corroded aluminum sample were identified using PECT through the capture and analysis of changes in phase and amplitude. The results show that saturation of thermal response from larger defects overshadows smaller outlying defects, suggesting the need for an alternative approach to characterizing defects smaller than 1 mm in diameter. Despite inherently longer measurement times, phase measurements during the cooling period result in higher sensitivity thus enabling detection of smaller defects. This study helps to define a range of operation and measurement parameters for PECT to be effective as a means for corrosion detection on aluminum painted panels. The outcomes pave the way to further extend PECT as a non-destructive, non-contact method for detection of CUI.
•In the present study, we investigate non-destructive evaluation of rebar corrosion by applying AE activity and electrochemical noise in reinforced concrete. The following innovative results are ...derived.•Acoustic emission (AE) activity is surely observed due to peeling of oxide film on the surface of rebar.•Pitting index of electrochemical noise (EN) method is applicable to detect pitting corrosion on the rebar surface.•By combining both findings, the process of rebar corrosion in concrete can be phenomenologically evaluated at an early stage.
Rebar corrosion was evaluated by monitoring reinforced concrete specimens. An electrical corrosion test accelerated the corrosion, and non-destructive evaluation of the acoustic emission (AE) and electrochemical noise (EN) was applied. Because AE phenomena were detected during rebar corrosion, the rise time and maximum amplitude increased, showing waveform variations. The AE activity was attributed to oxide film peeling of the rebar surface. The pitting index of the EN analysis ranged from –1 to 0, indicating localized corrosion. The EN method can detect pitting corrosion on rebar surfaces. AE and EN can be combined to phenomenologically evaluate early-stage rebar corrosion in concrete.
•Developed the CNN-based deep learning algorithm for a real-time detection of the existence and location of delamination in laminated composites using HNSW signals generated from a granular crystal ...sensor in a non-destructive manner.•Investigated the influences of the hidden layer and other CNN parameters such as learning rate, activation function, dropout, input image pixel size, batch size, and filter size to improve the accuracy of the deep learning algorithm. Furthermore, a general fitting curve (see Eq. (5)) that can be used for the optimal choices of the input pixel and batch sizes was developed.•Investigated the efficiency and accuracy of three different types of the input signals, i.e., original (raw) without pre-processing and two pre-processed signals (i.e., time-sliced and time-sliced noise-cutting signals), for real-time detection of detects using HNSWs. Moreover, we provided mathematical formulations to obtain time-sliced signals in Eq. (1) and time-sliced noise-cutting signals in Eq. (2) from pre-processing of the original HNSW signals.•Developed a multiple mode testing scheme, classifying defects using multiple HNSW signals instead of using a single HNSW signal, to improve the classification accuracy of the deep learning algorithm.
This paper proposes a real-time non-destructive evaluation technique to detect defects in laminated composites by deep learning using highly nonlinear solitary waves (HNSWs). HNSW data are collected by conducting experiments using a granular crystal sensor composed of a vertical array of steel beads directly contacting an AS4/PEEK composite plate. Using HNSW data, a deep learning algorithm based on the convolution neural networks (CNN) is trained and tested for the identification of delamination in AS4/PEEK composites. The influence of the number of hidden layers and various CNN parameters is investigated for improved classification accuracy of the deep learning algorithm. A general curve fit is presented in order to facilitate the correct choice of the input pixel and batch size. Moreover, a multiple mode testing scheme, classifying defects using multiple HNSW signals, is introduced to improve the accuracy of the algorithm. The efficiency and accuracy of using three different types of the input signal (i.e., original (without pre-processing) and time-sliced/time-sliced noise-cutting signals (with pre-processing)) are examined for the real-time detection of defects. Mathematical formulations are established to obtain time-sliced and time-sliced noise-cutting signals from the original HNSW signals. It was found that accuracy could be improved by increasing both the number of hidden layers and the input pixel size, reducing the learning rate, and by using a batch normalization process and RELU activation function. For all three input signals, accuracy levels of over 90% were achieved in identifying the existence and location of delamination in AS4/PEEK composites, highlighting the possibility of using the proposed deep learning algorithm for the real-time detection of defects in laminated composites.
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