Detection Of Internal Defects Research Articles

Internal defect detection model based on laser ultrasonic signal decomposition and deep learning

It is essential to accurately detect the internal defects of metal equipment. The accuracy of traditional detection methods is affected by noise. This paper proposes an innovative genetic algorithm-weighted permutation entropy (GA-WPE) optimized Variational mode Decomposition (VMD), continuous wavelet Transform (CWT) and deep learning fusion model, which aims to achieve efficient automatic defect detection. Firstly, the laser ultrasonic experimental system was built, and the hole defects with different diameters and depths were prepared. The GA-WPE-VMD was used to decompose and reconstruct the ultrasound signal, and CWT converted the reconstructed signal into a time–frequency scalogram. Three deep learning models were used for defect detection. ViT model achieved 99.88%, 97.01%, and 91.7% accuracy on the training, validation, and test set, respectively, significantly better than DenseNet121 and ResNet50. The AUC of ViT on the test set was close to 100%. The results verify the feasibility and effectiveness of the proposed method.

Fast reconstruction 3D computed tomography image of stacked cell under faster scanning by dual-branch cross-fusion flat bottom network

Abstract Cone beam computed tomography (CBCT) fast scanning and reconstruction is a key step to achieve rapid detection of internal defects in batteries. In this work, we have achieved a faster CT scanning just in 5 seconds by reducing the X-ray exposure time in sparse view CT. However, the CT data is extremely incomplete by faster scanning; the existing reconstruction methods are difficult to reconstruct a high quality three-dimensional (3D) CT image of stacked cells. To address this issue, we propose a 3D CT image reconstruction network, which can reconstruct higher quality CT images from low quality 3D volume data. The input data of the reconstruction network is not 2D projection data, but 3D volume data. In this network, a high and low resolution dual-branch cross-fusion flat bottom structure is designed. The high resolution flat bottom branch aims to preserve detailed information, while the low resolution flat bottom branch focuses on capturing more semantic information. Cross-fusion between these branches mitigates the loss of semantic details. Additionally, the auxiliary loss function, the main loss function, and the 3D attention module are designed to enhance semantic accuracy and the learning performance of the network. The 3D training data is collected under a fast scanning strategy spanning 5-60 seconds. During the training phase, we use clipping block technology to cut the 3D volume data, enabling direct training on the 3D volume data. Our experimental results demonstrate that our 3D reconstruction network outperforms mainstream algorithms under this faster scanning strategy, which is able to reconstruct higher quality 3D CT images just in 15 seconds. Ablation experiments confirm the positive impact of the dual-branch cross-fusion flat bottom structure, attention module, and loss functions on improving the quality of 3D CT images.

Efficiency analysis of laser ultrasonic longitudinal wave detection based on the constraint effect

Abstract Laser ultrasound is a non-destructive inspection technique, but its application in internal defect detection and state characterization is limited by its weak body wave displacement induced by thermo-elastic mechanism. In this paper, the influence of the constraint layer on the directivity of laser induced ultrasonic force source and the excitation efficiency of longitudinal wave is analyzed by finite element modeling with transparent glass as the constraint layer. With the optimization of the parameters of the confinement layer, the longitudinal waves with energy comparable to that of the free surface ablation mechanism can be excited under the thermoelastic mechanism, which greatly improves the efficiency of the longitudinal wave of laser ultrasonic. Applying the above research results to the detection of internal defects, the imaging results of the internal defects are obvious and the location defects are accurate.

Internal Thread Defect Generation Algorithm and Detection System Based on Generative Adversarial Networks and You Only Look Once.

In the field of industrial inspection, accurate detection of thread quality is crucial for ensuring mechanical performance. Existing machine-vision-based methods for internal thread defect detection often face challenges in efficient detection and sufficient model training samples due to the influence of mechanical geometric features. This paper introduces a novel image acquisition structure, proposes a data augmentation algorithm based on Generative Adversarial Networks (GANs) to effectively construct high-quality training sets, and employs a YOLO algorithm to achieve internal thread defect detection. Through multi-metric evaluation and comparison with external threads, high-similarity internal thread image generation is achieved. The detection accuracy for internal and external threads reached 94.27% and 93.92%, respectively, effectively detecting internal thread defects.

Tree internal defects detection method based on ResNet improved subspace optimization algorithm

The erosion behavior of trunk borers leads to the destruction of trunk structure and the formation of internal defects, which significantly impacts the ecological and economic value of trees. Traditional non-destructive testing (NDT) methods are costly and have low resolution, whereas electromagnetic NDT methods are more suitable for high-resolution detection and imaging. However, solving the highly nonlinear electromagnetic inverse scattering problems (ISPs) for small-sized defects with high contrast is challenging. Therefore, this paper proposes an improved subspace optimization algorithm based on a ResNet network called SOM-ResNet. SOM-ResNet incorporates physical principles into deep learning networks by simulating the iterative process of induced current and contrast, thereby enhancing its ability to accurately detect small objects with high contrast. Experimental results demonstrate that SOM-ResNet outperforms single inversion algorithms in detecting complex scatterers with small to medium-sized targets, validating its excellent performance.

Internal Hole Defect Detection Based on Laser Ultrasonic Shear Wave

Internal Hole Defect Detection Based on Laser Ultrasonic Shear Wave

Ultrasonic dynamic plane wave imaging for high-speed railway inspection

The detection of internal defects within railway tracks stands as a critical aspect of ensuring transport security, given its potential to precipitate severe accidents. Phased array ultrasonic testing methods have gained extensive usage in rail inspection owing to their remarkable precision and expansive coverage. Traditional ultrasonic inspection techniques typically require either a fixed relative position between the transducer and the specimens or a gentle movement of the transducer to beamform. However, the unavoidable vibration stemming from the vehicle–rail coupled interactions often results in time-variant media, significantly impacting the beamforming. This paper presents a refined wavenumber-domain plane wave imaging technique tailored to enable real-time dynamic imaging, specifically accommodating the vertical vibrational movement of the transducer. Real-time calculation of the transmission focal law is achieved by numerical fitting. The wavenumber-domain technique is utilized to realize real-time accurately reconstructed images. The efficacy of the proposed method is assessed through simulations and experiments performed on a convex cylindrical specimen and a rail head sample, respectively. It can be seen that this approach can achieve a frame rate of 83.3 fps with a pipeline architecture in this study, demonstrating the capability to reconstruct images in vibrating environments, such as during the high-speed inspection of railways.

Tire defect detection based on low and high-level feature fusion

Recently, object detection based on deep learning has made great progress in the field of defect detection. Due to its complex texture background and varied defect features, existing defect detection methods based on object detection face great challenges in the detection of tire internal defects. In this paper, a tire defect detection model based on low and high-level feature fusion is proposed. First, a multi-head feature extraction module is proposed, which extracts abundant effective information from multiple dimensions. Second, a spatial semantic fusion upsampling module is proposed to alleviate the problem of information loss in the upsampling process. Finally, a novel prediction head is designed to expand the receptive field by compressing the size of the feature map to improve the detection accuracy of large defects. Experimental results show that the mAP of the proposed method achieves 94.03% on the tire internal defect dataset, and the average detection time is 36.74 ms, which can meet the needs of industrial online detection.

Three-Dimensional Profile Reconstruction and Internal Defect Detection of Silicon Wafers Using Cascaded Fiber Optic Fabry–Pérot Interferometer and Leaky Field Detection Technologies

Abstract Wafer quality control is one of the important processes to improve the yield rate of semiconductor products. Profile quality and defects in the wafer are two key factors that should be taken into consideration. In this research, we introduce a method that measures the profile of the upper surface and the thickness of the wafer at the same time using an optical fiber cascaded Fabry–Pérot interferometer working at wavelength of 1550 nm. Therefore, the 3D profile of the wafer can be reconstructed directly. Testing results show that both accuracy and precision of the Fabry–Pérot interferometer are within a nanometer scale. Defects, especially those embedded inside the wafer, will be detected by monitoring the leaky field with treating wafers as slab waveguides. With the leaky field detection, defects on the lower surface of the wafer were successfully detected by monitoring the leaky field above the upper surface of the wafer. Compared with traditional methods such as radiographic testing or computed tomography testing, the proposed methods provide a cost-effective alternative for wafer quality evaluation.

Internal thread defect detection system based on multi-vision.

In the realm of industrial inspection, the precise assessment of internal thread quality is crucial for ensuring mechanical integrity and safety. However, challenges such as limited internal space, inadequate lighting, and complex geometry significantly hinder high-precision inspection. In this study, we propose an innovative automated internal thread detection scheme based on machine vision, aimed at addressing the time-consuming and inefficient issues of traditional manual inspection methods. Compared with other existing technologies, this research significantly improves the speed of internal thread image acquisition through the optimization of lighting and image capturing devices. To effectively tackle the challenge of image stitching for complex thread textures, an internal thread image stitching technique based on a cylindrical model is proposed, generating a full-view thread image. The use of the YOLOv8 model for precise defect localization in threads enhances the accuracy and efficiency of detection. This system provides an efficient and intuitive artificial intelligence solution for detecting surface defects on geometric bodies in confined spaces.

Detection the internal quality of watermelon seeds based on terahertz imaging technology combined with image smoothing and enhancement algorithm

AbstractThe cultivation processes of watermelon seed are often affected by issues such as empty shells and defects, resulting in significant losses. To obtain high‐quality seeds, the terahertz imaging technology combined with image smoothing and enhancement algorithm was proposed to reduce the noise and non‐obvious features caused by the influence in the imaging process and realize the non‐destructive, efficient, and accurate detection of the internal quality of watermelon seeds. Initially, a terahertz imaging system with a spatial resolution of 0.4 mm was used to acquire images of watermelon seeds with varying levels of fullness. Subsequently, denoising techniques, including Gaussian filtering, median filtering, bilateral filtering, discrete wavelet transformation denoising, wavelet denoising, and principal component analysis denoising, were used to handle the terahertz spectral images of watermelon seeds in the frequency range of 1–1.5 THz, respectively. Image enhancement operations, involving segmented linear gray‐level transformation and fractional‐order differentiation, were performed on the terahertz images of watermelon seeds after denoising. The optimal image processing approach was determined based on defect assessment through threshold segmentation. Finally, the validation was conducted at a spatial resolution of 0.2 mm. The images at a spatial resolution of 0.4 mm were subjected to wavelet denoising and window slicing in segmented linear gray‐level transformation (WS‐SLT) enhancement; the results exhibited the following improvements in defect accuracy compared with untreated THz images. A 7.74% increase in accuracy was observed for empty seeds, along with a 6.29% increase in the defect ratio for defective seeds 1. The defect ratio for intact seeds was 0, and there was no significant difference in defect ratio accuracy for defective seeds 2. At a spatial resolution of 0.2 mm, the average defect ratio error of THz imaging handled by wavelet denoising and WS‐SLT was approximately 5.04%. In conclusion, the terahertz imaging technology coupled with wavelet denoising and WS‐SLT methods can be used to enhance the accuracy of internal defect detection in watermelon seeds, and it provides a technical foundation and reference for assessing watermelon seed fullness.

Toward robust and high-throughput detection of seed defects in X-ray images via deep learning

BackgroundThe detection of internal defects in seeds via non-destructive imaging techniques is a topic of high interest to optimize the quality of seed lots. In this context, X-ray imaging is especially suited. Recent studies have shown the feasibility of defect detection via deep learning models in 3D tomography images. We demonstrate the possibility of performing such deep learning-based analysis on 2D X-ray radiography for a faster yet robust method via the X-Robustifier pipeline proposed in this article.Results2D X-ray images of both defective and defect-free seeds were acquired. A deep learning model based on state-of-the-art object detection neural networks is proposed. Specific data augmentation techniques are introduced to compensate for the low ratio of defects and increase the robustness to variation of the physical parameters of the X-ray imaging systems. The seed defects were accurately detected (F1-score >90%), surpassing human performance in computation time and error rates. The robustness of these models against the principal distortions commonly found in actual agro-industrial conditions is demonstrated, in particular, the robustness to physical noise, dimensionality reduction and the presence of seed coating.ConclusionThis work provides a full pipeline to automatically detect common defects in seeds via 2D X-ray imaging. The method is illustrated on sugar beet and faba bean and could be efficiently extended to other species via the proposed generic X-ray data processing approach (X-Robustifier). Beyond a simple proof of feasibility, this constitutes important results toward the effective use in the routine of deep learning-based automatic detection of seed defects.

Morphology reconstruction and defect detection of holes inner surface based on reflection characteristics

A reconstruction method is proposed based on the reflection characteristics to measure the defect profile and depth of the inner surface of the hole. In addition to capturing geometric information of the scene, this method decomposes the distortion in the endoscope image into circumferential and axial distortions, thereby improving correction accuracy and enabling accurate measurement of the defect contour. The bidirectional reflection distribution function (BRDF) is established based on reflection characteristic theory, allowing for mapping between image brightness and normal declination. By accumulating tangent values of normal deflection angles, the height of axial contour lines inside the hole can be reconstructed to achieve defect shape reconstruction and depth measurement. This approach overcomes convex-concave ambiguity in shape from shading (SFS), as demonstrated by experimental results. The proposed detection method offers equipment versatility and high measurement efficiency, making it suitable for machine vision-based detection of internal surface defects in holes.

Nondestructive detection of fruit fly (Bactrocera dorsalis) infestation in Alphonso and Totapuri varieties of mango fruits by thermal imaging technique

AbstractThermal imaging is a noncontact and nondestructive sensing technique that provides thermal signatures of objects by detecting the infrared radiations emitted by them above 1 K. The nondestructive detection of internal defects and infestations has vast applications in the Food Industry. In the present experiment, a C3 thermal camera (FLIR systems) having a thermal sensitivity of <0.10°C was used for the detection of fruit fly infestation in mango fruits. The healthy mango fruits of both varieties had a uniform surface temperature with a variation of only ±0.1°C. The maximum temperature difference between the healthy and fruit fly infested regions in fruit fly infested Totapuri mango was found to be 4.56°C in the heated and dark conditions. The maximum difference in the surface temperature of 4.3°C was observed between the healthy and infested regions of the infested Alphonso mango under heated and dark conditions. The difference in temperature between the healthy and infested regions of mangoes can be attributed to the changes in the internal structure, thermal properties, and biochemical properties of the healthy and infested regions of the infested mango fruits. The statistical analysis showed a significant difference (p < 0.05) in surface temperature between the infested and healthy regions of fruit fly infested Alphonso and Totapuri mango fruits. This envisages that nondestructive fruit fly infestation in Alphonso and Totapuri varieties of mango fruits can be detected under active thermography.Practical ApplicationsThe quality of mango fruit can be described in terms of external and internal quality. The external quality parameters such as color, size, and weight are usually considered during the sorting of mangoes. The internal defects such as diseases, physiological disorders, and damage due to pests are also significant in mango processing. The visual inspection methods for detecting pest infestation in fruits are nonreliable, time‐consuming, and subjective. Therefore, there is a need for efficient, reliable, and accurate methods to detect fruit fly infestation in mango fruits. A nondestructive method for the detection of fruit fly infestation has the potential to increase fruit quality and acceptability, thereby benefiting processors, traders, and consumers. Thermal imaging has a distinct advantage over other nondestructive quality assessment techniques as it is noncontact, practically feasible, and economically viable.

Monitoring of Älvkarleby test embankment dam using 3D electrical resistivity tomography for detection of internal defects

Electrical resistivity tomography (ERT) is a potential-based method for detecting internal erosion in the core of embankment dams using the electrodes installed outside. This study aims at evaluating the practical capability of ERT monitoring for detecting internal defects in embankment dams. A test embankment dam with in-built well-defined defects was built in Älvkarleby, Sweden, to assess different monitoring systems including ERT and the defect locations were unknown to the monitoring teams. Between 7500 and 14,000 ERT data points were acquired daily, which were used to create the distribution of electrical resistivity models of the dam using 3D time-lapse inversion. The inversion models revealed a layered resistivity structure in the core that might be related to variations in water content or unintentional variations in material properties. Several anomalous zones that were not associated with the defects were detected, which might be caused by unintentional variations in material properties, temperature, water content, or other installations. The results located two out of five defects in the core, horizontal and vertical crushed rock zones, with a slight location shift for the horizontal zone. The concrete block defect in the core was indicated, although not as distinctly and with a lateral shift. The two remaining defects in the core, a crushed rock zone at the abutment and a wooden block and a crushed rock zone in the filter, were not discovered. The results cannot be used to fully evaluate the capability of ERT in detecting internal erosion under typical Swedish conditions due to limited seepage associated with the defects. Furthermore, scale effects need to be considered for larger dams.

Reliability of Noninvasive Sonic Tomography for Detection and Quantification of Internal Defects in Yoshino Cherry

Abstract Preventive maintenance through monitoring and early detection is important for trees that are structurally weak due to internal decay. Once internal decay has occurred, recovery of lost strength is irreversible. The decaying tree becomes vulnerable to external impacts, which may lead to breakage or windthrow. The Yoshino cherry (Cerasus×yedoensis (Matsum.) “Somei-yoshino”) accounts for a high proportion of landscape trees in Asia. Testing the reliability of noninvasive sonic tomography (SoT) to estimate internal defects could facilitate structural stability evaluation and proactive preventive management. We performed noninvasive SoT and invasive resistance microdrilling to detect and quantify internal defects in old and large Yoshino cherry trees. Generally, SoT reflected the areas and location of severe structural defects, showing a correlation with the resistance microdrilling results (R² = 0.542, p < .001). However, when cracks were present in disks, the SoT overestimated defects and errors mainly observed in the transition zone (green). Based on these findings, when evaluating the SoT, careful decisions are needed to distinguish between actual defects and broad sonic shadows. Furthermore, additional evidence from drilling resistance is required to differentiate between incipient decay and cracks. The results of this study provide valuable insights for enhancing the interpretation of sonic tomograms. Study Implications: We suggest defect boundaries in sonic tomograms should be redefined to improve the accuracy of interpretation for any new species being studied. Careful drilling resistance measurements should be taken, especially in transition zones (green areas in tomogram) and incipient decay zone (light brown areas). Noninvasive sonic tomograms only reflect acoustic properties of the tested cross-section of a tree and cannot be considered an actual representation of the internal conditions until the readings are calibrated to destructive sampling results.

Research Status and Progress on Non-Destructive Testing Methods for Defect Inspection of Micro-Electronic Packaging

Abstract In a highly competitive and demanding micro-electronics market, non-destructive testing (NDT) technology has been widely applied to defect detection and evaluation of micro-electronic packaging. However, the trend of micro-electronic packaging toward miniaturization, high-density, ultrathin, ultralight, and with small chip footprint, poses an urgent demand for novel NDT methods with high-resolution and large penetration depth, which is utilized for internal defect detection and identification of advanced complicated packages. The conventional NDT methods for micro-electronic packaging mainly include optical visual inspection, X-ray inspection, active infrared thermography, scanning acoustic microscopy (SAM), atomic force microscopy (AFM), laser Doppler vibration measuring technique, scanning SQUID microscopy (SSM), electrical impedance spectroscopy, scanning electron microscope (SEM), and so on. This paper aims to provide a review of addressing their basic principles, advantages, limitations, and application researches in the field of defect inspection of micro-electronic packaging. Moreover, in order to overcome the shortcomings of the existing NDT methods, this paper emphasizes a novel NDT approach, called hybrid ultrasonic-laser digital holographic microscopy (DHM) imaging inspection method, and discusses its basic principle, merits, key techniques, system construction, and experimental results in detail. When some key technical problems can be solved in further research, this method will become a potentially promising technique for defect detection and evaluation of advanced complicated packages.

Accuracy improvement of inner defects of cylindrical components using ultrasonic detection with modified ALOK method

The accuracy of defect localisation and its size quantification is poor in the detection of internal defects of cylindrical components using the ultrasonic Amplituden und Laufzeit Orts-Kurven (ALOK) method. The influence of acoustic beam spread is not taken into consideration in the ultrasonic ALOK method, resulting in difficulties with the precise characterisation of the defect state. To address this, the relationship between the acoustic distance, amplitude, ultrasonic frequency, size and depth of hole defects was studied. The acoustic distance curve and the amplitude curve were fitted and then the localisation model of the defect was obtained. The acoustic beam spreading angle and echo sound pressure were introduced and then the size quantification model for defects was acquired based on principal component analysis (PCA). Both the simulated and experimental results show that the modified ALOK algorithm improved the detection accuracy of the defect location and its size and the relative error of defect sizing decreased by more than 35% compared with the original algorithm.

Non-Destructive Testing of Laser Welded Hydrogen Storage Tank Liner Using Optical Coherence Tomography

The Type 4 hydrogen storage tank has an internal liner made of polymer material. Instead of the joining of two cylinder-shaped liners through butt-joint thermal fusion bonding, a faster joining can be achieved through lap-joint laser welding. Non-destructive testing is required to inspect the quality of laser-welded areas and to detect pore defects. In this study, the interface of a laser-welded region is investigated using optical coherence tomography to detect internal pore defects, where the maximum thickness of the liner is found to be approximately 3 mm. Optical coherence tomography images with defects are then used for supervised YOLOv2 deep learning. Real-time detection of internal defects is successfully performed on laser-welded liner samples.

Research on electric field characteristics under different length interface air gap defects in cable terminals of high-speed train

It is vital that accurately and effectively detecting the insulation status of cable terminal to ensure the safe and stable operation of high-speed trains. Accordingly, a novel monitoring system for the internal defect detection of cable terminal was proposed based on electric field strength. Firstly, partial discharge and electric field strength along the surface of the air gap defects cable terminal umbrella skirt were measured and analyzed. The development of internal defects in cable terminals can be divided into three stages: S1, S2, and S3. Then, the interquartile range, variance, average value and dispersion coefficient of electric field strength have been proposed to characterize the development state of air gap defects in cable terminal. The results shows that the internal defects of cable terminals develop from 20%l to 100%l, the slope of the interquartile range of the distribution of magnitudes of the electric field intensity increases from 0.126 to 0.671, and the absolute value of variance and offset increase by about 81.3% and 80.66%, respectively. Therefore, it is feasible and effective to detect the development state of air gap defects inside the cable terminal by measuring and analyzing the electric field strength characteristics along the surface of the cable terminal umbrella skirt.