Transient-spatial pattern mining of eddy current pulsed thermography using wavelet transform

Manually selected frames have been used for both defect detection and quantification in Eddy current pulsed thermography. Defects are indicated by high/low temperatures within frames. However, variation of surface emissivity sometimes introduces illusory temperature inhomogeneity and results in detection false alarms. To improve the probability of detection, this paper proposes a two heat balance states-based method. For each pixel, a surface emissivity coefficient is extracted from the two heat balance states and is used to correct the transient response. The temperature and emissivity is thus separated. A verified experiment was carried out on a steel sample having both a polished and black strip painted surface, and the image quality was improved significantly.

Recently, basalt-carbon hybrid composite structures have attracted increasing attention due to their better damage tolerance, if compared with carbon fiber-reinforced polymer composites (CFRP). Low-velocity is considered as one of the most severe threats to composite materials as it is usually invisible and it occurs frequently in service. With this regard, nondestructive testing (NDT) techniques, especially emerging modalities, are expected to be an effective damage detection method. Eddy current-pulsed thermography (ECPT), as an emerging NDT technique, was used to evaluate the damage induced by low-velocity impact loading in a CFRP laminate, as well as in two different-structured basalt-carbon hybrid composite laminates. In addition, ultrasonic C-scan and x-ray computed tomography were performed to validate the thermographic results. Pulsed phase thermography, principal component thermography, and partial least squares thermography were used to process the thermal data and to retrieve the damage imagery. Then, a further analysis was performed on the imagery and temperature profile. As a result, it is concluded that ECPT is an effective technique for hybrid composite evaluation. The impact energy tends to create an interlaminar damage in a sandwich-like structure, while it tends to create an intralaminar damage in an intercalated stacking structure.

Pulsed eddy current thermography can detect surface or subsurface defects in steel, but in the process of combining deep learning, it is expensive and inefficient to build a complete sample of defects due to the complexity of the actual industrial environment. Consequently, this study proposes a transfer learning method based on Twin-NMF and combines it with the SimAM attention mechanism to enhance the detection accuracy of the target domain task. First, to address the domain differences between the target domain task and the source domain samples, this study introduces a Twin-NMF transfer method. This approach reconstructs the feature space of both the source and target domains using twin non-negative matrix factorization and employs cosine similarity to measure the correlation between the features of these two domains. Secondly, this study integrates a parameter-free SimAM into the neck of the YOLOv8 model to enhance its capabilities in extracting and classifying steel surface defects, as well as to alleviate the precision collapse phenomenon associated with multi-scale defect recognition. The experimental results show that the proposed Twin-NMF model with SimAM improves the detection accuracy of steel surface defects. Taking NEU-DET and GC10-DET as source domains, respectively, in the ECTI dataset, mAP@0.5 reaches 99.3% and 99.2%, and the detection accuracy reaches 98% and 98.5%.

Carbon fiber reinforced polymer (CFRP) has become an essential structural material in aerospace, new energy, rail transportation, and other fields due to its lightweight and high-strength properties. However, its complex manufacturing processes and demanding service environments can lead to various types of damage on both the surface and inside the material, significantly compromising safety. Existing detection techniques often adopt a step-by-step inspection mode, which results in complex processes and makes it difficult to achieve cross-dimensional correlation analysis of defects, leading to blind spots in structural health assessment. To address these issues, this paper proposes a method for simultaneous detection of surface and subsurface defects in CFRP based on infrared polarization imaging. First, we developed an infrared polarization imaging-based defect detection system and collected a CFRP defect dataset of samples containing various surface and subsurface defects. Then, we proposed a surface and subsurface defect detection network, which integrates extracted thermal diffusion features and polarization features to decouple and enhance surface and subsurface features. Through the constraints of multi-task defect detection, simultaneous detection of surface and subsurface defects is achieved. The experiments on the collected CFRP defect dataset demonstrate the effectiveness of the proposed method.

Nondestructive thermography is a high-speed, low-cost, and safe solution for subsurface defects detection of carbon fiber reinforced polymer (CFRP) materials, providing essential quality control in aerospace, automobile, and sports industries. In this article, we build a reflective lock-in thermography system and construct a dataset that contains real-captured thermal image sequences of CFRP samples with various simulated internal defects under different excitation frequencies. Then, we present a novel 3-D convolutional neural network (CNN) model incorporating a combination of spatial and temporal convolutional filters and batch-size independent group normalization (GN) as a unified framework to process thermal image sequences captured by lock-in thermography for simultaneous subsurface defect detection and depth estimation. Finally, we define a multitask loss function to perform end-to-end training of both defect detection and depth estimation tasks based on the real-captured infrared sequences. Comparative experiments are carried out on CFRP specimens with artificial defects of various sizes/shapes and at different depths. Qualitative and quantitative results illustrate that our 3-D CNN model is capable of predicting accurate locations and depths of subsurface defects and performs favorably against the hand-crafted and CNN-based methods in lock-in thermography for individual defect detection and depth estimation tasks. The captured dataset and the source codes will be made publicly available.

Carbon fiber-reinforced polymer (CFRP) laminates are widely used in aerospace, automotive, and infrastructure industries due to their high strength-to-weight ratio. However, defect detection in CFRP remains challenging, particularly in low signal-to-noise ratio (SNR) conditions. Conventional segmentation methods often struggle with noise interference and signal variations, leading to reduced detection accuracy. In this study, we evaluate the impact of thermal image preprocessing on improving defect segmentation in CFRP laminates inspected via pulsed thermography. Polynomial approximations and first- and second-order derivatives were applied to refine thermographic signals, enhancing defect visibility and SNR. The U-Net architecture was used to assess segmentation performance on datasets with and without preprocessing. The results demonstrated that preprocessing significantly improved defect detection, achieving an Intersection over Union (IoU) of 95% and an F1-Score of 99%, outperforming approaches without preprocessing. These findings emphasize the importance of preprocessing in enhancing segmentation accuracy and reliability, highlighting its potential for advancing non-destructive testing techniques across various industries.

Deep learning-assisted real-time defect detection and closed-loop adjustment for additive manufacturing of continuous fiber-reinforced polymer composites

Pulsed thermography (PT) has emerged as a widely adopted technology for internal defect detection in carbon fibre reinforced polymer (CFRP) composites owing to its cost-effectiveness and rapid detection capabilities. However, defect identification in PT testing remains challenging because of the significant non-uniform heat distribution background and noise interference within the thermal images. This study introduces a novel thermographic defect detection approach that combines a multi-scale wavelet transform with sparse principal component thermography (MWT-SPCT). In this method, a multi-scale wavelet transform (MWT) is first applied to the thermal images for band-pass filtering. By leveraging the multi-resolution analysis capability of wavelet transform, the MWT simultaneously eliminates low-frequency background and high-frequency noise components. Following this stage, SPCT is employed to extract the features of defects based on the dynamic variation of defect signals throughout the sequence. Finally, through binarization of the extracted features, accurate defect segmentation and localization can be obtained. The experimental results demonstrated that, the MWT preprocessing selectively isolates defect information, thus increasing the signal-to-noise values of the defect signals in the filtered results. Compared to SPCT frameworks incorporating preprocessing methods such as contrast enhancement, background equalization, and signal smoothing, the proposed MWT SPCT framework yields significantly improved detection performance. Moreover, generalization evaluations demonstrate that MWT SPCT maintains consistently high accuracy and strong statistical stability across repeated measurements under different inspection conditions. This validates the reliability of the MWT-SPCT framework for defect detection under the influence of background and noise interference.

Carbon fiber-reinforced polymers (CFRPs) are widely used in the fabrication of solid rocket motor casings due to their exceptional performance. However, the bonding interface between CFRP and viscoelastic materials (rubber) is prone to debonding damage during service and storage under complex environmental conditions, which poses a significant threat to the structural integrity and reliability of the engine. Existing nondestructive testing (NDT) methods, such as X-ray imaging, infrared thermography, and ultrasonic testing, although somewhat effective, exhibit significant limitations in detecting interfacial defects in deep or multilayered composite materials, particularly under the challenging conditions of service and storage. This study proposes an innovative method based on active Lamb wave energy analysis and introduces the Damage Evolution Factor (DEF), specifically designed to detect and evaluate interfacial debonding defects in CFRP-rubber bonded structures within solid rocket motors during service and storage. Through numerical simulations and experimental validation, we selected the A0 mode Lamb wave, which is more sensitive to interfacial damage, as the incident wave and excited it on the surface of the structure. Displacement time-history response signals at observation points under different damage models were extracted and analyzed, and DEF values were calculated. The results show that DEF values increase with the size of the interfacial debonding damage. Similar trends were observed in experimental studies, further validating the effectiveness of this method and demonstrating that DEF can be used for the quantitative evaluation of interfacial debonding defects in CFRP-rubber bilayer bonded structures.

A portable fiber laser thermography system with beam homogenizing for CFRP inspection

Failing to release unneeded system resources such as I/O streams can result in resource leaks, which can lead to performance degradation and system crashes. Existing resource-leak detectors are usually based on predefined defect patterns to detect resource leaks in software. However, they typically report too many false positives and negatives, and also lack effective warning prioritization. Our empirical investigation shows that, their predefined defect patterns are not precise enough, and moreover, their used defect detection processes are not suitable enough for the defect patterns. In our approach, we introduce a novel Expressive Defect Pattern Specification Notation (EDPSN). With EDPSN, a resource-leak defect pattern can be defined more precisely by specifying conditional method calls and more expressively by including guiding information for the defect detection and warning prioritization process, such as the characteristics of its preferred defect detection process and the effective prioritization impact factors for its related warnings. Based on the EDPSN-based defect pattern, our approach tries to flexibly tune out a suitable defect detection and warning prioritization process. Through evaluations on three real-world projects (Eclipse-3.0.1, JBoss-3.0.6, and Weka-3.6.4), we show that our approach achieves high average precision (96%) and recall (74%), 26% and 49% higher than existing approaches, respectively.

Carbon fiber-reinforced polymer (CFRP) cables have been widely applied in modern engineering due to their excellent mechanical properties and durability, and their service safety has received increasing attention. However, there is currently a lack of efficient and accurate methods for detecting defects in CFRP cables. This article proposes a CFRP cable defect detection method based on electromagnetic tomography (EMT) technology to provide technical support for ensuring the safe service of cables. Due to the ill-posedness and instability of the inverse problem of EMT, there are reconstruction errors in the image reconstruction results. To address the EMT image reconstruction problem, an L1 regularization image reconstruction algorithm based on the proximal gradient method is proposed, which improves the defect reconstruction ability and reconstruction accuracy of the algorithm. First, the objective function is gradually optimized by combining gradient descent and proximal mapping operations to achieve variable-step-size gradient descent to handle the solution problems caused by the nonsmooth terms of the regularization function. On this basis, the image color mapping is adjusted to suppress the artifacts and noise in the reconstructed image. Finally, the proposed method is verified by carbon fiber composite material cables with different defect distributions. The numerical and experimental results show that the proposed method has stronger defect detection ability and can obtain high-quality defect reconstruction results. Compared with traditional methods, the image evaluation indicators have increased by 29.6% and 51.7%, respectively.

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

Defects detection within a composite component, with the aim of understanding and predicting its mechanical behavior, is of great importance in the aeronautical field because the irregularities of the composite material could compromise functionality. The aim of this paper is to detect defects by means of non-destructive testing (NDT) on T-pull samples made by carbon fiber reinforced polymers (CFRP) and to evaluate their effect on the mechanical response of the material. Samples, obtained from an industrial stringer having an inclined web and realized with a polymeric filler between cap and web, were subjected to ultrasonic monitoring and then to T-pull mechanical tests. All samples were tested with the same load mode and the same test configuration. An experimental set-up consisting of a semiautomatic C-scan ultrasonic mapping system with a phased array probe was designed and developed, optimizing control parameters and implementing image processing software. The present work is carried out on real composites parts that are characterized by having their intrinsic defectiveness, as opposed to the previous similar results in the literature mainly obtained on composite parts with artificially produced defects. In fact, although samples under study were realized free from defects, ultrasonic mapping found defectiveness inside the material. Moreover, the ultrasonic inspection could be useful in detecting both the location and size of defects. Experimental data were critically analyzed and qualitatively correlated with results of T-pull mechanical tests in order to better understand and explain mechanical behavior in terms of fracture mode.

Defect detection in CFRP structures using pulsed thermographic data enhanced by penalized least squares methods