Local Defects Research Articles

SCS-YOLO: A Defect Detection Model for Cigarette Appearance

Appearance defects significantly impact cigarette quality. However, in the current high-speed production lines, manual inspection and traditional methods are unable to satisfy the actual demands of inspection. Therefore, a real-time and high-precision defect detection model for cigarette appearance, SCS-YOLO, is presented. The model integrates space-to-depth convolution (SPD-Conv), a convolutional block attention module (CBAM), and a self-calibrated convolutional module (SCConv). SPD-Conv replaces the pooling structure to enhance the granularity of feature information. CBAM improves the ability to pay attention to defect locations. Improved self-calibrated convolution broadens the network’s receptive field and feature fusion capability. Additionally, Complete IoU loss (CIoU) is replaced with Efficient IoU Loss (EIoU) to enhance model localization and mitigate sample imbalance. The experimental results show that the accuracy of SCS-YOLO is 95.5% and the mAP (mean average precision) value is 95.2%. Compared with the original model, the accuracy and mAP value of the SCS-YOLO model are improved by 4.0%. Furthermore, the model achieves a detection speed of 216 FPS, meeting cigarette production lines’ accuracy and speed demands. Our research will positively impact the real-time detection of appearance defects in cigarette production lines.

Polarization-dependent photoemission electron microscopy for domain imaging of inorganic and molecular materials.

Polarization-dependent photoemission electron microscopy (PD-PEEM) exploits spatial variation in the optical selection rules of materials to image domain formation and material organization on the nanoscale. In this Perspective, we discuss the mechanism of PD-PEEM that results in the observed image contrast in experiments and provide examples of a wide range of material domain structures that PD-PEEM has been able to elucidate, including molecular and polymer domains, local electronic structure and defect symmetry, (anti)ferroelectricity, and ferromagnetism. In the end, we discuss challenges and new directions that are possible with this tool for probing domain structure in materials, including investigating the formation of transient ordered states, multiferroics, and the influence of molecular and polymer order and disorder on excited state dynamics and charge transport.

Differential analysis of the impact of lesions’ location on clinical and radiological outcomes after the implantation of a novel aragonite-based scaffold to treat knee cartilage defects

Abstract Purpose There is limited comparative evidence on patient outcomes following cartilage repair in various knee compartments. The aim of this study was to compare clinical and imaging outcomes after treating cartilage defects in femoral condyles and trochlea with either an aragonite-based scaffold or surgical standard of care (SSoC, i.e., debridement/microfractures) in a large multicentre randomized controlled trial. Methods 247 patients with up to three knee joint surface lesions (ICRS grade IIIa or above) in the femoral condyles, trochlea or both (“mixed”), were enrolled and randomized to surgery with either a cell-free aragonite scaffold or SSoC. Patients were followed for up to 48 months by analysing subjective scores (KOOS and IKDC), radiological outcomes (defect filling on MRI), as well as treatment failure rates and adverse events. A differential analysis of outcomes for condylar, trochlear and mixed lesions was performed. Results The scaffold group significantly outperformed the SSoC group regardless of lesion location with statistically significantly better KOOS Overall scores at 24 months (all p ≤ 0.0009) and 48 months (all p ≤ 0.02). Similar results were observed for KOOS subscales and IKDC scores. For KOOS responder rates, superiority of the implant group was demonstrated at 24, 36, and 48 months (all p ≤ 0.004). Higher defect filling on MRI for implants was observed for all locations. Lower treatment failure rates for the implant were observed in condylar and mixed lesions. Conclusion The aragonite-based scaffold was safe and effective regardless of the defect location, providing superior clinical and radiological outcomes compared to SSoC up to four years follow-up. Level of evidence I – Randomized controlled trial.

Defect-induced localization of information scrambling in 1D Kitaev model

We discuss one-dimensional(1D) spin compass model or 1D Kitaev model in the presence of local bond defects. Three types of local disorders concerning both bond-nature and bond-strength that occur on kitaev materials have been investigated. Using exact diagonalization, two-point spin-spin structural correlations and four-point Out-of-Time-Order Correlators(OTOC) have been computed for the defective spin chains. The proposed quantities give signatures of these defects in terms of their responses to location and strength of defects. A key observation is that the information scrambling in the OTOC space gets trapped at the defect site giving rise to the phenomena of localization of information scrambling thus making these correlators a suitable diagnostic tool to detect and characterize these defects.

Bridge Surface Defect Localization Based on Panoramic Image Generation and Deep Learning-Assisted Detection Method

Applying unmanned aerial vehicles (UAVs) and vision-based analysis methods to detect bridge surface damage significantly improves inspection efficiency, but the existing techniques have difficulty in accurately locating damage, making it difficult to use the results to assess a bridge’s degree of deterioration. Therefore, this study proposes a method to generate panoramic bridge surface images using multi-view images captured by UAVs, in order to automatically identify and locate damage. The main contributions are as follows: (1) We propose a UAV-based image-capturing method for various bridge sections to collect close-range, multi-angle, and overlapping images of the surface; (2) we propose a 3D reconstruction method based on multi-view images to reconstruct a textured bridge model, through which an ultra-high resolution panoramic unfolded image of the bridge surface can be obtained by projecting from multiple angles; (3) we applied the Swin Transformer to optimize the YOLOv8 network and improve the detection accuracy of small-scale damages based on the established bridge damage dataset and employed sliding window segmentation to detect damage in the ultra-high resolution panoramic image. The proposed method was applied to detect surface damage on a three-span concrete bridge. The results indicate that this method automatically generates panoramic images of the bridge bottom, deck, and sides with hundreds of millions of pixels and recognizes damage in the panoramas. In addition, the damage detection accuracy reached 98.7%, which is improved by 13.6% when compared with the original network.

MT-U2Net: Lightweight detection network for high-precision magnetic tile surface defect localization

Automated surface defect localization is closely related to product quality control. Although many image segmentation models have achieved excellent results in industrial defect localization, the wide-ranging defects in magnetic tile materials and the similarity between textures and background textures make it difficult to balance detection model performance and model size. Therefore, we propose a new magnetic tile defect localization model called MT-U2Net. The model consists of three parts: Dres-Ublock, U2 structure, and attention mechanism. Dres-Ublock is used to enhance the model's feature extraction capability and reduce its size; the U2-Net structure is utilized to learn cross-scale defect information; the attention mechanism is employed to eliminate redundant information. MT-U2Net improves detection performance across various defect scales. In the test set, MT-U2Net achieves an F1 Score of 0.840 and an MAE of 0.024 and compresses the model size to 3.39 M. Extensive experimental results show that MT-U2Net outperforms existing state-of-the-art algorithms in magnetic tile defect detection performance and model size.

Localization and Physical Analysis of Defects in Degraded Power HEMT p-GaN Transistors Stressed with DC Voltage Surge and Voltage with Switching Stress

Localization and Physical Analysis of Defects in Degraded Power HEMT p-GaN Transistors Stressed with DC Voltage Surge and Voltage with Switching Stress

Exploring the magnetic behavior of potassium-doped Cu0.5Tl0.5Ba2Ca2Cu3-xKxO10-δ (x=0, 1, 2.5, 3) superconductors

This study investigates the superconducting and magnetic behaviour of Potassium-doped Cu0.5Tl0.5Ba2Ca2Cu3-xKxO10-δ superconductors by varying potassium-concentrations (x = 0, 1, 2.5, 3). We have synthesized the samples by two-step solid-state-reaction method and explored their superconducting features by XRD analysis, SEM and resistivity measurements. Through a comprehensive exploration of magnetic measurements i.e magnetization versus magnetic field and temperature, we aim to elucidate the impact of potassium doping on the diamagnetic characteristics of these superconductors. The results reveal that by raising the doping concentration of potassium, the orthorhombic unit cell's axis lengths decresese. The onset of superconductivity and the magnitude of diamagnetism are also decreased. The magnetic behavior of these samples was studied by field cooled and zero field cooled from 2 to 300 K and taking the hysteresis loops at temperatures 2 to 60 K. In the doped samples, the suppression of current density is steeper with increase in temperature. Doped atoms boost the population of localized defects, which serve as pinning centers. This study will contribute valuable information to the understanding of the relationship between magnetic behavior and the superconducting nature of complex oxide materials.

Decision Tree Clusters: Non-destructive detection of overheating defects in porcelain insulators using quantitative thermal imaging techniques

Porcelain insulators are subject to performance deterioration during operation under the influence of complex environments, thus increasing the occurrence of flashover accidents. Regular inspection of insulator condition is of great significance for the stable operation of power grids. Therefore, we propose a non-destructive detection method of insulator overheating defects using quantitative thermography. It can be used for defect detection of porcelain insulators with internal overheating defects. In this study, the relationship between the insulating properties of porcelain insulators and the heating anomalies was first analyzed. Then, Decision Tree Clusters (DTC) algorithm is used to extract the spatial temperature feature information to achieve the defective insulator location capture. Finally, Submodular-pick Local Interpretable Model-agnostic Explanations (SLIME) is used for model decision visualization to verify the feasibility of the technique. The experimental results show that DTC not only ensures the accuracy of insulator long-distance detection, but also realizes the quantitative detection of insulators.

Breast-Conserving Surgery with Immediate Partial Breast Reconstruction by Using Pedicled Thoracodorsal Artery Perforator Flap: A 7-Year Follow-Up of 50 Women

Introduction: Thoracodorsal artery perforator (TDAP) flap has been used for more than 10 years in the immediate partial breast reconstruction (IPBR) of breast cancer, allowing many patients to maximize reserved autologous breast tissue who do not have indications for breast-conserving surgery (BCS). No large sample size survival data for this operation have been reported worldwide. Methods: There are 212 primary breast cancer patients who were prepared to receive BCS with IPBR of pedicled TDAP flaps in our institution from June 2013 to December 2017. Finally, the operations were completed successfully in 50 cases. All patients were female with a median age of 40 years. Results: The average diameter of TDAPs measured by Doppler ultrasound before the operation was 1.8 ± 0.6 mm (ranging from 0.6 to 3.3 mm). The average size of the flaps was 15 × 6 cm. The average time of operations was 282 ± 71 min (ranging from 120 to 425 min). The drainage tube was removed 4.8 ± 1.9 days after the operation (ranging from 3 to 12 days). All TDAP flaps survived, and the wound complication rate was 4% (2/50). The median follow-up time was 88 (67, 94) months (M [IQR]), with a 5-year ipsilateral breast tumor recurrence rate of 4% (95% CI: 0.1–9.5%), a 5-year breast cancer-free interval of 88.0% (95% CI: 79.0–97.0%), and a 5-year overall survival rate of 96.0% (95% CI: 90.5–99.9%). Conclusion: IPBR using pedicled TDAP flaps is a good choice for repairing local breast defects in BCS. The advantages of this surgery are no influence on latissimus dorsi function, few complications in the donor area, and good long-term prognosis.

Printing of 3D photonic crystals in titania with complete bandgap across the visible spectrum.

A photonic bandgap is a range of wavelengths wherein light is forbidden from entering a photonic crystal, similar to the electronic bandgap in semiconductors. Fabricating photonic crystals with a complete photonic bandgap in the visible spectrum presents at least two important challenges: achieving a material refractive index > ~2 and a three-dimensional patterning resolution better than ~280 nm (lattice constant of 400 nm). Here we show an approach to overcome such limitations using additive manufacturing, thus realizing high-quality, high-refractive index photonic crystals with size-tunable bandgaps across the visible spectrum. We develop a titanium ion-doped resin (Ti-Nano) for high-resolution printing by two-photon polymerization lithography. After printing, the structures are heat-treated in air to induce lattice shrinkage and produce titania nanostructures. We attain three-dimensional photonic crystals with patterning resolution as high as 180 nm and refractive index of 2.4-2.6. Optical characterization reveals ~100% reflectance within the photonic crystal bandgap in the visible range. Finally, we show capabilities in defining local defects and demonstrate proof-of-principle applications in spectrally selective perfect reflectors and chiral light discriminators.

MFAD-RTDETR: A Multi-Frequency Aggregate Diffusion Feature Flow Composite Model for Printed Circuit Board Defect Detection

To address the challenges of excessive model parameters and low detection accuracy in printed circuit board (PCB) defect detection, this paper proposes a novel PCB defect detection model based on the improved RTDETR (Real-Time Detection, Embedding and Tracking) method, named MFAD-RTDETR. Specifically, the proposed model introduces the designed Detail Feature Retainer (DFR) into the original RTDETR backbone to capture and retain local details. Subsequently, based on the Mamba architecture, the Visual State Space (VSS) module is integrated to enhance global attention while reducing the original quadratic complexity to a linear level. Furthermore, by exploiting the deformable attention mechanism, which dynamically adjusts reference points, the model achieves precise localization of target defects and improves the accuracy of the transformer in complex visual tasks. Meanwhile, a receptive field synthesis mechanism is incorporated to enrich multi-scale semantic information and reduce parameter complexity. In addition, the scheme proposes a novel Multi-frequency Aggregation and Diffusion feature composite paradigm (MFAD-feature composite paradigm), which consists of the Aggregation Diffusion Fusion (ADF) module and the Refiner Feature Composition (RFC) module. It aims to strengthen features with fine-grained awareness while preserving a certain level of global attention. Finally, the Wise IoU (WIoU) dynamic nonmonotonic focusing mechanism is used to reduce competition among high-quality anchor boxes and mitigate the effects of the harmful gradients from low-quality examples, thereby concentrating on anchor boxes of average quality to promote the overall performance of the detector. Extensive experiments are conducted on the PCB defect dataset released by Peking University to validate the effectiveness of the proposed model. The experimental results show that our approach achieves the 97.0% and 51.0% performance in mean Average Precision (mAP)@0.5 and mAP@0.5:0.95, respectively, which significantly outperforms the original RTDETR. Moreover, the model reduces the number of parameters by approximately 18.2% compared to the original RTDETR.

Investigating Flow-Induced Corrosion of Magnesium in Ophthalmological Milieu.

Although the impact of local fluid dynamics in the biodegradation of magnesium is well known, currently no studies in the literature address the degradation effects of ocular vitreous on bioresorbable devices made of magnesium, which could be developed as drug delivery carriers. The aim of this study was to investigate the flow-induced corrosion mechanism of magnesium in an ophthalmological environment for future applications in ophthalmic drug delivery. To achieve this, experimental and computational methods were combined. Specifically, a CFD model was employed to design experimental conditions that replicate the ocular flow-induced shear stress (FISS) on manufactured magnesium samples. Pure Mg samples were tested in a bioreactor system capable of imposing the ocular CFD calculated values of FISS on the Mg samples' surface by varying the pump flow rate. Optimal flow rates for a range of different FISS values specific to the ophthalmological fluid dynamics affecting the device were indeed determined before running the experiments. After conducting customized corrosion tests, morphological observations and profilometric maps of the eroded surfaces of Mg samples were obtained using scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM). These maps were then post-processed for the parametric evaluation of corrosion rates. Pre-existing localized superficial defects did affect the final corrosion pattern. SEM images and CLSM data confirmed a uniform corrosion mechanism, with corrosion rates of 1.9, 2.7, and 3.4 μm/day under different shear stress conditions (0, 0.01, and 0.032 Pa, respectively). More generally, uniform corrosion on pure Mg samples increased with higher FISS values, and at higher shear stress values (FISS = 0.032 Pa), a notable washing-out effect of the corrosion products was observed. The removal of corrosion products at higher shear stresses suggests that the dynamic ocular environment, influenced by saccadic movements, plays a significant role in the corrosion mechanism of pure magnesium. The corrosion rates determined in this study, in conjunction with clinical drug release requirements, are crucial for designing potential drug-release devices for ocular applications.

A two-stage defect detection method for unevenly illuminated self-adhesive printed materials

The process of printing defect detection usually suffers from challenges such as inaccurate defect extraction and localization, caused by uneven illumination and complex textures. Moreover, image difference-based defect detection methods often result in numerous small-scale pseudo defects. To address these challenges, this paper proposes a comprehensive defect detection approach that integrates brightness correction and a two-stage defect detection strategy for self-adhesive printed materials. Concretely, a joint bilateral filter coupled with brightness correction corrects uneven brightness properly, meanwhile smoothing the grid-like texture in complex printed material images. Then, in the first detection stage, an image difference method based on a bright–dark difference template group is designed to effectively locate printing defects despite slight brightness fluctuations. Afterward, a discriminative method based on feature similarity is employed to filter out small-scale pseudo-defects in the second detection stage. The experimental results show that the improved difference method achieves an average precision of 99.1% in defect localization on five different printing pattern samples. Furthermore, the second stage reduces the false detection rate to under 0.5% while maintaining the low missed rate.

Nanotube spin defects for omnidirectional magnetic field sensing

Optically addressable spin defects in three-dimensional (3D) crystals and two-dimensional (2D) van der Waals (vdW) materials are revolutionizing nanoscale quantum sensing. Spin defects in one-dimensional (1D) vdW nanotubes will provide unique opportunities due to their small sizes in two dimensions and absence of dangling bonds on side walls. However, optically detected magnetic resonance of localized spin defects in a nanotube has not been observed. Here, we report the observation of single spin color centers in boron nitride nanotubes (BNNTs) at room temperature. Our findings suggest that these BNNT spin defects possess a spin S = 1/2 ground state without an intrinsic quantization axis, leading to orientation-independent magnetic field sensing. We harness this unique feature to observe anisotropic magnetization of a 2D magnet in magnetic fields along orthogonal directions, a challenge for conventional spin S = 1 defects such as diamond nitrogen-vacancy centers. Additionally, we develop a method to deterministically transfer a BNNT onto a cantilever and use it to demonstrate scanning probe magnetometry. Further refinement of our approach will enable atomic scale quantum sensing of magnetic fields in any direction.

An insight on local defect resonance based on modal decomposition analysis: A two-dimensional case

The resonance behaviors of defects, specifically local defect resonance (LDR), are useful for defect detection in structures. However, there is a lack of intuitive elucidation regarding the underlying mechanisms governing defect resonance. This study aims to analyze the LDR of a horizontal crack in a plate activated by Lamb waves using the modal decomposition method (MDM). The MDM involves an approximate decomposition of the wavefield into finite constituent Lamb wave modes, including propagating, non-propagating, and inhomogeneous modes. The amplitude of each mode is determined according to the continuous boundary conditions of displacement and stress between the damaged zone and the intact zone. Analytical results reveal specific frequencies at which the damaged zone exhibits significantly stronger vibrations than the intact zone, indicating the emergence of LDR. The corresponding wavefields exhibit various resonance patterns, encompassing both in-plane LDR and out-of-plane LDR types. In contrast to finite element simulations and experimental observations, the MDM unveils the dominant constituents of resonance patterns: out-of-plane LDR results from the combination of reflected and transmitted A0 and A1 modes within the damaged zone, while in-plane LDR is formed by the superposition of reflected and transmitted A0, A1, and S0 modes.

Bearing fault detection using second-order moment spectrum and adaptive time-varying morphological filtering

The second-order moment spectrum is a method designed to simplify the complex shape of the spectrum, thus facilitating its interpretation for the identification and localization of defects based on peak frequency. Generally used as a final step in defect detection methods, this method offers the advantage of a more easily interpretable spectral shape. Compared to the shape of the spectrum of the vibration signal defined by the Fourier transform, which includes sidebands composed of peaks of large amplitude at different frequencies, the spectrum generated by the second-order moment spectrum method stands out for its simplicity. Starting from the mean and standard deviation of the vibration signal, the second-order moment can be defined as the power of the ratio between the standard deviation and the difference between the signal and the mean. Next, the Fourier transform is applied to express the second-order moment spectrum. The performance of the second-order moment spectrum is evaluated using the principle of comparison with the envelope spectrum obtained by the Hilbert transform. Vibration signals are analyzed using two methods: adaptive time-varying morphological filtering and second-order moment spectrum. After applying these methods to the signals from the database, we observe high-amplitude peaks at the frequencies corresponding to inner ring and ball defects. The second-order moment spectrum gives similar results to those obtained with the Hilbert transform envelope.

Thick piezoelectric films by aerosol deposition at room temperature: corona poling and force sensing

In this study, we employed corona poling to improve the piezoelectric properties of as-deposited BaTiO3 films and conducted a vibration energy harvesting test. Dielectric measurements indicated that the dielectric constant of the as-deposited film increased with temperature, and the frequency dependence of the dielectric constant was minimal at room temperature. Applying an electric field of 1500 kV cm−1 resulted in a recoverable energy density of 7.1 J cm−3 and an energy storage efficiency of 54%. The corona polarization treatment could align dipoles under high electric fields and prevent dielectric breakdown owing to local defects created by the aerosol deposition (AD) process. The vibration test yielded a harvested energy of 172 nJ and an output voltage of 2.67 V, which is suitable for force sensor applications. Polarization via corona discharge is also feasible without an electrode. Integrating AD with corona poling may benefit new capacitors, sensors, and energy harvesting technologies.

Towards diagnostics of aerospace structural defects using a novel physics-based post-processing scheme employing lock-in thermography

During its service life, an aircraft experiences various loadings that affect its structural performance. Thin sheet riveted joints are ubiquitous in aircraft and require thorough inspections to arrest any defects that may cause catastrophic failure. Subsequently once flown off, loose and/or cracked rivets may get stuck in critical areas such as flight controls or get ingested into the engine and become a source of Internal Object Damage (IoD). While several inspection schemes are employed in the aviation industry for rivets-related defect detection, most of them are subjective and skill-dependent. Towards the aim of developing an efficient rivet defects diagnostic technique, in this research work, a novel physics-based post-processing scheme employing lock-in thermography (LIT) is proposed for the detection of such defects. For this purpose, a hierarchical experimental thrust has been adopted, where in the beginning the scheme was developed on the lab experiments which was further extended/refined to detect defects on actual aircraft. Two different post-processing techniques have been used for the interpretation of LIT, namely the Fast Fourier Transform (FFT) technique and an indigenously developed temporal temperature gradient-based approach to capture defects whereby localized thermal emissivity variance at the defect location affects the local thermal conductivity and the surface temperature profile. The study considers two common defect i.e., looseness and sub-surface cracks in rivets. The results obtained from the novel post-processing technique provide empirical understanding, better quantification, and clearer comprehension of the two types of rivets-related defects by making it more objective as compared to the FFT technique. The proposed novel methodology in this work exhibits higher fidelity than conventional FFT by accurately identifying the rivet defects thereby providing a promising alternative to in-vogue visual inspections.

Near Stochiometric LiNbO3 Crystal: the Piezoelectric Features and the Shear Horizontal Guided Wave Transducer for Structural Health Monitoring up to 650 °C.

The application of shear horizontal (SH) guided wave transducers in high-temperature structural health monitoring (SHM) is a topic of significant interest across various industrial engineering sectors. In this study, we utilized the novelty piezoelectric crystal of near stoichiometric lithium niobate (NSLN), which exhibited a robust piezoelectric response (d15 = 77.6 pC/N@room temperature). Next, the pure thickness shear vibration mode d15' through size optimization was designed. It was demonstrated that the NSLN-based ultrasonic guided wave transducers utilizing the optimum d15' mode were proficient in transmitting and receiving pure fundamental SH wave (SH0 wave) along two orthogonal main directions (0° and 90°) over a wide frequency range (100-350 kHz), exhibiting strong response to the SH0 wave. Under the driving voltage of 100 V, the signal voltages of the NSLN-based transducer were found to be on the order of 200.3 and 11.8 mV at room temperature and high temperature of 650 °C, respectively. Moreover, the NSLN-based SH0 transducer showcased its better defect localization ability, and the signal-to-noise ratio (SNR) sensitivity of NSLN-based transducer was evaluated to be 16.1 dB at high temperature of 650 °C. To sum up, the ultrasonic wave transducer based on NSLN crystal demonstrated higher potential applications for in situ SHM under elevated temperatures.