Detection Of Subsurface Defects Research Articles

Road sub-surface defect detection based on gprMax forward simulation-sample generation and Swin Transformer-YOLOX

Road sub-surface defect detection based on gprMax forward simulation-sample generation and Swin Transformer-YOLOX

Nondestructive detection of CFRP subsurface defects using transient lock-in thermography

Nondestructive detection of CFRP subsurface defects using transient lock-in thermography

TOFD detection of shallow subsurface defects in aluminum alloy thin plates by half-skip mode-converted wave

Aluminum alloy is widely applied to the aerospace field. However, the inspection of thin plates using Time-of-Flight Diffraction (TOFD) technique is restricted by the near-surface dead zone because of the coupling between diffracted longitudinal wave and lateral wave. The half-skip mode-converted wave is introduced to decrease dead zone and detect defects in aluminum alloy thin plates by increasing ray path and propagation time. The quantitative correlation for the diffracted shear wave from longitudinal back-wall wave is deduced in combination with the acoustic path, realizing the accurate location of shallow subsurface defects. Simulated and experimental results indicate that the dead zone is decreased by 38% by the half-skip mode-converted wave, and the location errors are within 5% for the aluminum alloy plate with a thickness of 7.0 mm. Compared to other alternative TOFD techniques, half-skip mode-converted wave has better response amplitude and positioning accuracy, demonstrating strong applicability in TOFD inspection of thin plates.

Fluorescence modulation of quantum dots in subsurface defects of optical elements by a linearly polarized light.

The limited excitation efficiency of quantum dots in the detection of subsurface defects in optical elements by quantum dot fluorescence gives rise to insufficient accuracy. To enhance the excitation efficiency of quantum dots, we studied the modulation of the polarization direction of linearly polarized incident light on quantum dot fluorescence. We first apply density matrix evolution theory to study the quantum dots interacting with linearly polarized incident light and emitting fluorescence. The fluorescence intensity exhibits cosine oscillations versus modulated laser polarization. It reaches a maximum value at the polarization angle zero, and then decreases as the angle becomes larger until π/2. The experimental results for the quantum dot in both solutions and subsurface defect of optical elements confirmed these results. For optical elements tagged with CdSe/ZnS quantum dots, the fluorescence intensity increases by 61.7%, and the area for the detected subsurface defects increases by 142.9%. Similarly, for C and InP/ZnS quantum dots, there are also increases in both the fluorescence intensity and the area of subsurface defects. Our study suggests that the subsurface defect detection in optical elements by the linearly polarized incident light could enhance the detection accuracy of subsurface defects in optical elements, and potentially achieve super-resolution imaging of subsurface defects.

Automatic detection of quartz glass subsurface defects by laser scattering method based on an ellipsoidal mirror.

With the improvement of quality requirements of optical components, the detection of subsurface defects of optical components has become a key technology. The existing detection methods still have some limitations in detection depth and detection efficiency. In this paper, a defect scattering light collection method based on ellipsoidal mirror model is used to analyze the scattering light collection efficiency under different experimental conditions theoretically, and the favorable conditions for improving the scattering light collection are proposed. After simulation verification, the use of ellipsoidal reflectors to collect scattered light can effectively avoid the impact of surface defects compared to lenses. At the same time, an experimental system based on this method is set up to filter the stray light by mean filtering method. The system detected three scratches (2μm in width and 252nm in depth) on the underside of a piece of quartz glass. The results show that the system can clearly detect the subsurface defects of optical components.

Real-time hollow defect detection in tiles using on-device tiny machine learning

This study addresses the challenge of subsurface defect detection in floor tiles for quality control in residential construction. To overcome the limitations of traditional inspection methods and the complexities associated with existing artificial intelligence (AI)-based approaches, we have developed the AI diagnostic Stick (AID-Stick), a novel tool designed to advance the field of tile defect detection. This innovative tool integrates an embedded machine-learning framework, leveraging convolutional neural networks and tiny machine learning techniques. The AID-Stick utilizes spectrogram, Mel-frequency cepstral coefficient, and Mel filterbank energy for real-time, on-microcontroller unit diagnostics of auditory signals from tile tapping tests. Our methodology effectively utilizes these acoustic features in distinguishing between intact and subsurface hollow defective tiles. The study’s findings, revealing a notable validation accuracy of 97% and a real-world accuracy of 81.25%, showcase a promising improvement over traditional methods. The AID-Stick’s practicality, cost-effectiveness, and user-friendly design make it potentially beneficial for small-to-medium enterprises and economically constrained markets. Furthermore, this research opens avenues for future enhancements in embedded AI systems, with potential applications extending beyond the construction industry to other domains requiring non-destructive testing. This work not only contributes to the field of industrial quality control but also to the development of intelligent diagnostic tools, paving the way for future innovations in automated inspection technologies.

Application of laser scanning thermography and regression analysis to determine characteristics of defects in polymer composite materials

The method of laser point scanning thermography is highly sensitive and allows for reliable detection of surface and subsurface defects in products made of polymer composite materials. When implementing this method, the use of robotic manipulators as a scanning device makes it possible to inspect small objects with a curved surface or to further examine questionable areas identified by other methods. The article provides information about the layout of a robotic complex for laser scanning thermography based on a five-axis robotic manipulator, laser power up to 3 W and wavelength 405 nm, as well as a COX CG640 thermal imager. A technique for processing experimental data has been proposed and regression models have been developed to make it possible to measure the size of defects along the trajectory and determine their location. To test the protocol, a control sample was made from fiberglass laminate, including artificial defects of the “delamination” type, in the form of squares of various sizes. The coefficient of determination R2 of regression models turned out to be no worse than 0.94, the root mean square error of the defect model and the transverse size were no worse than ±0.2 and ±1.5 mm2, respectively.

Subsurface Defect Detection in GPR Data Integrating Temporal and Spatial Features

Subsurface Defect Detection in GPR Data Integrating Temporal and Spatial Features

Cross-Correlation Inspired Residual Network for Pulsed Eddy Current Imaging and Detecting of Subsurface Defects

The decay of pulsed eddy currents (PEC) with depth in diffusion decreases resultant signal changes, thus bringing a challenge for reliable and accurate evaluation of deep subsurface defects. In this work, a novel cross-correlation inspired residual network, termed CCResNet, is proposed to improve the capability for smart evaluation of subsurface defects. It consists of a cross-correlation layer, a residual network, and a novel loss function, namely, focal-probability of detection (Focal-POD) loss. The customized Gaussian wavelet basis enables us to derive weak features from heavily noised PEC signals due to the similarity by cross-correlation operation, which is the origin of the constructed cross-correlation layer. Then, a Focal-POD loss is proposed to address class imbalance and endow CCResNet with powerful capability for detection of deep subsurface defects by increasing their loss values. Finally, a semi-supervised framework is built to re-train CCResNet using pseudo and labelled dataset to obtain classified results as imaging features. The experimental results show that the developed CCResNet is featured as better imaging resolution, more accurate evaluation, and intelligence in detection of deeper subsurface defects.

Feature Recognition in Quadratic Frequency Modulated Thermal Wave Imaging for Subsurface Defect Detection in Fiber-Reinforced Polymers

Feature Recognition in Quadratic Frequency Modulated Thermal Wave Imaging for Subsurface Defect Detection in Fiber-Reinforced Polymers

Identification of early decayed oranges using structured-illumination reflectance imaging coupled with fast demodulation and improved image processing algorithms

Effective detection of decayed oranges (Citrus genus) at the early stage is challenging because there are no or few visual symptoms on the infected fruit. Structured-illumination reflectance imaging (SIRI) has been proven effective for enhanced detection of subsurface defects in fruit. Amplitude component (AC) images retrieved from the original SIRI patterned images are useful for defect detection, but generally require acquiring three phase-shifted pattern images, which limits the imaging and detection speed. Moreover, the AC images may also suffer from noticeable uneven brightness due to fruit curvature, which affects the identification of decayed areas. This study was therefore aimed to explore faster image demodulation, enhancement and processing algorithms, based on a specially developed SIRI technology, for effective identification of early decayed oranges. Pattern images were acquired, using three phase-shifted sinusoidal illumination patterns at the wavelength of 810 nm and a spatial frequency of 0.20 cycles mm−1, from the orange samples infected with Penicillium digitatum fungus, the most serious and devastating pathogen for orange fruit. Two-dimensional spiral phase transform was used to obtain AC images from one or two pattern images. The acquired AC images were then processed by using simple brightness adjustment and integral image-based fast average filtering for brightness correction, and improved watershed algorithm and global threshold for segmentation of decayed areas. Three different combinations of these image processing procedures for single and two pattern images were proposed to distinguish decayed oranges from sound ones. The three methodologies all achieved high overall identification rates of 97.5%, 95.0% and 95.3%, when the stem-end effect was also considered. This study showed that accurate detection of early decayed orange fruit can be achieved by using one or two phase-shifted pattern images, which would be beneficial for real-time implementation of the technique.

Artificial Neural Network Based Sub-surface Defect Detection in Glass Fiber Reinforced Polymers: Nondestructive Evaluation 4.0

Artificial Neural Network Based Sub-surface Defect Detection in Glass Fiber Reinforced Polymers: Nondestructive Evaluation 4.0

A novel microwave-based non-destructive testing system for detection of sub-surface defects in natural flax fiber reinforced composite material

In the work presented here, microwave non-destructive testing using a rectangular waveguide loaded with a novel planar unit cell filter for the inspection of defects in the composite is studied. The proposed planar filter is designed as a unit cell with a copper (annealed) microstrip component on an FR-4 substrate. The waveguide loaded with the planar filter exhibits a reflection coefficient of −35.919 dB at a frequency of 10.548 GHz. This planar filter waveguide is used as the sensor probe for the non-destructive testing method proposed here and is aimed to increase the sensitivity by measuring the differences in permittivity of materials. Computer simulation technology software (CST studio suite) is used for implementing the design, simulation, and material/defect evaluation. The defects studied are voids, cracks, and delamination in an epoxy-based natural flax fiber-reinforced composite material. The performance-improved microwave sensor probe is used to pick up permittivity variations in the presence of defects in lax-based natural fiber-reinforced composite material.

Differentiate tensor low rank soft decomposition in thermography defect detection

Composites are prone to defects in manufacture，which are to be evaluated for safety through non-destructive testing (NDT) techniques. Thermal images are acquired for NDT by using Optical pulsed thermography. Defect detection can be performed by proposing defect detection algorithms. However, due to the low resolution of defect contrast, the detection performance of the existing algorithm is still sub-optimal. In this work, a decomposition algorithm by differentiating low-rank tensors is proposed to extract weak defect information from complex thermal pattern disturbances for surface and sub-surface defect detection. The algorithm mines deep insight into the information on the differentiation of different ranks between structures from the results of Tucker decomposition to extract defect features. In particular, a probabilistic tensor model is introduced to correct potential mismatch patterns enhance defect contrast, and suppress noise and light spot interference. To verify the effectiveness and robustness of the proposed algorithm, a variety of complex composite specimens have been used for validation. The experimental results show that the proposed algorithm achieves better performance compared to the state-of-the-art algorithms especially in enhancing the defect contrast and suppressing the light spot in seven common samples. In overall, it can provide on average of approximately 15% F-score and 3 dB SNR improvement for validation.

Influence of water film on subsurface defect detection using eddy current pulsed thermography

Eddy current pulsed thermography (ECPT) is an effective non-destructive testing (NDT) technique for detecting flaws in metallic materials. However, due to the low radiation and high reflection properties of metallic materials, as well as the inhomogeneous emission caused by the complex states of the material surface, the detection of subsurface defects becomes difficult. In this paper, the physical mechanisms underlying the interference of the thin water film in ECPT detection are studied. Two static comparative experiments have been carried out on the ferromagnetic plate with several artificial subsurface defects. In the first comparative experiment, a high radiation point has been artificially added near the defects. The experimental results showed that water film could eliminate the influence of inhomogeneous emission. In the second experiments, the effectiveness of water film in improving the detectability of subsurface defects has been demonstrated and the subsurface defects with a maximum buried depth of 0.4 mm can be detected.

A Magnetic Shielding-Type PEC Sensor With a Canister Structure and Magnetic Core

The pulsed eddy current (PEC) technique exhibits considerable benefits over conventional eddy current techniques. The structure and dimensional parameters of PEC sensors considerably influence the PEC systems. Conventional PEC sensors are susceptible to background noise of a magnetic excitation field. To strengthen the magnetic gathering ability and improve detection sensitivity, a magnetic shielding shell structure was introduced to PEC sensors. In this study, a novel canister structure and magnetic core-based PEC sensor with magnetic shielding was designed. Finite-element simulation and experimental study were performed subsequently to optimize the design. A 3-D finite-element simulation model was established according to the eddy current detection mechanism and electromagnetic shielding principle. The influence of the PEC sensor structure and size on its detection performance was observed. Next, a 2-D axis-symmetric model was established, and the size of this PEC sensor was optimized by using the orthogonal approach. The results revealed that the detection performance of this PEC sensor could be improved by incorporating an appropriate magnetic shielding mechanism and through the optimization of the sensor size. The research results show that compared with the PEC sensor without magnetic shield, the PEC sensor with magnetic shield developed in this article can obtain higher signal amplitude and can be better applied to surface and subsurface defect detection.

Two-stage convolutional neural network for joint removal of sensor noise and background interference in lock-in thermography

Lock-in thermography provides a fast, low-cost, and accurate non-destructive testing for subsurface defect detection in materials. However, there is serious sensor noise and background interference presented in the infrared images captured by lock-in thermography. In this paper, we set up lock-in thermography experiments to analyze the characteristics of sensor noise and background interference. Based on their different characteristics, we design a two-stage CNN model for the joint removal of sensor noise and background interference. This model consists of a number of multi-direction feature extraction blocks (MDFEBs) to extract local features in different directions for pixel-level sensor noise removal, and an encoder–decoder architecture with large receptive fields to extract global features for background interference correction. Experimental results demonstrate that our proposed method outperforms the well-established infrared image denoising and non-uniform background correction techniques on specimens made of different materials and at various excitation frequencies. The proposed two-stage CNN model provides high-quality input of infrared images for the following subsurface defect detection task in lock-in thermography.

Improved detection of subsurface defects through active thermography and ensembling techniques

Quality control and defect detection are major challenges in industrial environments. The localization of these defects is of crucial importance, as they can affect the performance of manufactured products and even the safety of people. Defect detection methods based on visual sensors and image processing are nowadays the most common approaches. To detect subsurface defects it is common to combine active thermography and deep learning. In this paper, PCT (Principal Components Thermography) is employed to enhance the SNR (signal-to-noise ratio), leading to a significant improvement in the results. Recent developments in this field apply deep learning models for semantic segmentation and object detection. However, the quality of the predictions is not enough to ensure that all quality controls are met. In this paper, a combination of semantic segmentation and object detection is proposed to increase the reliability of predictions. To carry out this combination, a working methodology and two new ensembling strategies are proposed. After comparing the results of the proposed combination with the results of using only semantic segmentation methods in a real industrial scenario, where carbon fiber sheets are used, it is found that the proposal improves the segmentation metrics by a 5% to a 24%. Thus the reliability of the predictions is improved.

Scanning inductive pulse phase thermography with changing scanning speed for non-destructive testing

ABSTRACT This work aims at non-destructive detection of surface and subsurface defects in metallic specimens (e.g. railway track components) via pulsed inductive thermography in a moving setup. A new approach for scanning with changing speed has been developed to enable manual scanning of components in track. A steel specimen is heated with an inductor, and the surface temperature, including its variation over time, is recorded with an infrared camera. The infrared sequence is evaluated by Fourier transform to phase image, in order to increase the image quality. The proposed method uses a referencing object with AprilTags visible in each frame, allowing an adjustment by image registration. Changes in velocity during the scanning process are possible, as demonstrated by a manually performed scan of the specimen and the corresponding evaluation. Results obtained with different infrared cameras and scanning speeds were condensed into an estimation of the maximum scanning speed for a given camera specification.

Detection, distinction, and quantification of pipeline surface and subsurface defects by DC magnetization scanning induction thermography

Pipelines are the backbone of the oil and natural gas industry. In order to guarantee its quality, DC magnetization scanning induction thermography (DCMSIT) is proposed to detect, distinguish and quantify the pipeline surface and subsurface defects. The exciting method, consisting of Helmholtz DC magnetization coils and a toroidal AC induction heating coil, is designed to adapt the pipeline’s annular shape and realize full coverage automatic detection. Under DC magnetization, surface and subsurface defects cause distorted permeability distribution in the skin layer relating to defect depth. Under AC magnetization, the distorted permeability produces abnormal thermal distribution, indicating the characteristics of defects. Based on the different thermal distributions in shapes and intensities of surface and subsurface defects, continuous wavelet transform (CWT) is used for thermal signal spatial-frequency analysis to distinguish surface and subsurface defects. The principle of the proposed method is analyzed based on Faraday’s law, Joule’s law, and thermal propagation. Then, finite element simulation is conducted to investigate magnetic permeability distribution for pipeline surface and subsurface defects with different depths. Finally, the DCMSIT system for pipelines is designed and developed to validate the proposed method. Experimental results show that the proposed method can evaluate pipeline surface and subsurface defects from 1.0 mm to 5.0 mm in depth.