A Dynamic Programming Algorithm for the Online Cutting Problem with Defects and Quality Grades

Ultrasonic guided waves are sensitive to many different types of defects and have been studied for defect recognition in rail. However, most fault recognition algorithms need to extract features from the time domain, frequency domain, or time-frequency domain based on experience or professional knowledge. This paper proposes a new method for identifying many different types of rail defects. The segment principal components analysis (S-PCA) is developed to extract characteristics from signals collected by sensors located at different positions. Then, the Support Vector Machine (SVM) model is used to identify different defects depending on the features extracted. Combining simulations and experiments of the rails with different kinds of defects are established to verify the effectiveness of the proposed defect identification techniques, such as crack, corrosion, and transverse crack under the shelling. There are nine channels of the excitation-reception to acquire guided wave detection signals. The results show that the defect classification accuracy rates are 96.29% and 96.15% for combining multiple signals, such as the method of single-point excitation and multi-point reception, or the method of multi-point excitation and reception at a single point.

Eddy current testing is a nondestructive testing method, which is used to detect discontinuities and defects in conductive materials. Using this technique, two different types of artificial defects in a railhead were evaluated in order to analyze the relationship between different types of defects and eddy current signals, and to obtain data on the size of the rail surface defects and crack location. The actually used rail sample was also studied. Surface cracks and defects were clearly observed as amplitude and phase changes of detected signals. This study succeeds in quantitatively analyzing and discriminating the damage types.

In the machining of precision components, a range of defects, including scratches, pits and uneven materials, will inevitably manifest on the surface of the components. Such defects not only result in a reduction in the quality and precision of the parts, but also have an impact on the overall quality, performance, and longevity of the equipment. It is therefore of particular importance to implement effective detection and repair procedures for surface defects in parts. The implementation of efficient detection methods enables the identification and rectification of defects in a prompt and effective manner, thereby ensuring the reliability and long-term stability of the equipment. In this work, we propose a deep neural network-based model that utilises deep convolutional neural networks (DCNNs) to extract features on the surface of parts, and gradually extracts the spatial and textural features of images through multiple convolutional layers and pooling layers. Subsequently, the aforementioned features are fed into the fully connected layer, where complex feature representations are further learned. Finally, the accurate classification of defect types is achieved through multilayer neural networks. The model is thus capable of effectively capturing and identifying different types of surface defects, thereby improving the accuracy and efficiency of identification. The experimental analysis demonstrates that the model exhibits high accuracy and robustness in the identification of different types of defects, thereby verifying its effectiveness and reliability in practical applications.

Flat glass products

In the actual industrial production environment, the surface defects of products are subtle, and the number of different types of defect data samples is also quite small. Most deep learning models rely on a large number of training samples and parameters to achieve high-precision defect detection. At the same time, the edge computing layer in the actual industrial environment may also encounter transmission delays and insufficient resources. Training a proper model for a specific type of surface defect while simultaneously satisfying the real-time accuracy of defect detection is still a challenging task. To effectively deal with the above challenges, we propose an edge-cloud computing defect detection model based on the intrinsic mean feature detector in the Lie Group space. The modules in the model adopt a symmetrical structure, which can extract related features more effectively. Different from existing models, this model utilizes the Lie Group space intrinsic mean feature as a metric to characterize the essential attributes of different types of surface defects. In addition, we propose an intrinsic mean attention mechanism in the Lie Group manifold space that is easy to implement at the edge service layer without increasing the number of model parameters, thereby enhancing the detection performance of tiny surface defects. Extensive experiments on three publicly available and challenging datasets reveal the superiority of our model in terms of detection accuracy, real-time detection, number of parameters, and computational performance. In addition, our proposed model also shows competitiveness and advantages compared with state-of-the-art models.

With rapidly growing photoconversion efficiencies, hybrid perovskite solar cells have emerged as promising contenders for next generation, low-cost photovoltaic technologies. Yet, the presence of nanoscale defect clusters, that form during the fabrication process, remains critical to overall device operation, including efficiency and long-term stability. To successfully deploy hybrid perovskites, we must understand the nature of the different types of defects, assess their potentially varied roles in device performance, and understand how they respond to passivation strategies. Here, by correlating photoemission and synchrotron-based scanning probe X-ray microscopies, we unveil three different types of defect clusters in state-of-the-art triple cation mixed halide perovskite thin films. Incorporating ultrafast time-resolution into our photoemission measurements, we show that defect clusters originating at grain boundaries are the most detrimental for photocarrier trapping, while lead iodide defect clusters are relatively benign. Hexagonal polytype defect clusters are only mildly detrimental individually, but can have a significant impact overall if abundant in occurrence. We also show that passivating defects with oxygen in the presence of light, a previously used approach to improve efficiency, has a varied impact on the different types of defects. Even with just mild oxygen treatment, the grain boundary defects are completely healed, while the lead iodide defects begin to show signs of chemical alteration. Our findings highlight the need for multi-pronged strategies tailored to selectively address the detrimental impact of the different defect types in hybrid perovskite solar cells.

The article deals with improving the quality of the cut flat glass. The aim of the study was to determine mechanism of origin temporary and permanent stress in the glass. This affects the quality of the cut flat glass. Of the commonly measured process parameters were adjusted following an evaluation, better cooling of flat glass between the furnace and the point of cutting into a prescribed format glass. The results are applicable for flat glass production lines.

Sideband instabilities and defects of quasipatterns

Background: This study discusses the use of wavelet transform for processing acoustic signals when diagnosing power transformers. A simulator with various defects was used to explore the application of continuous wavelet transform. The study also outlines an algorithm for obtaining signal standards to identify different types of defects. Aim. To develop a mathematical model for constructing reference acoustic control signals when processing data from a simulator with various defects. These signals will be used to identify and diagnose defects in real power transformers during their operation. Methods. The study introduces expressions for signal transformation, presents acoustic control signals, and explains the formation of reference signals. It also describes the process of recognizing the closest signals in form, corresponding to different types of defects. Results. The key results include expressions for signal transformation, acoustic control signals, and the formation of reference signals. The closest matching signals were identified, enabling accurate recognition of different defect types. Conclusion. The research proposes a method for processing acoustic control signals, allowing the reproduction of various insulation defects of power transformers and the recognition of such defects according to the proposed parameters of the wavelet transform.

Photovoltaic (PV) cells can be characterized using current-voltage (I-V) as well as electroluminescence (EL) characteristics. However, unlike I-V, the potential of EL characteristics has not been well discussed in the literature. In this work, the impact of different types of defects on the EL characteristics is studied, and the possible use of EL characteristics in the qualitative and quantitative investigation of defects is unveiled. Defective field-aged and new modules with artificially induced defects were taken for experimental study. EL current-intensity (I-ɸ) and EL voltage-intensity (V-ɸ) characteristics were studied for the cells suffering from major defects such as cracks, busbar interconnect failure, and PID-shunting. It is observed that I-ɸ characteristic is useful in the investigation of shunting defects, whereas V-ɸ characteristic is in diagnosing defect that increases series resistance. Bulk defects within a cell affect EL calibration constants and increase cell-to-cell mismatch within a module. The findings of this work would be useful for extracting valuable information from EL images regarding defects and degradations.

The substantial structural defects frequently observed in fabricated transition-metal dichalcogenide (TMD) samples inevitably affect the device performance. The molybdenum telluride (MoTe2) monolayer can easily generate phase transitions between the 1H and 1T' phases due to a small energy barrier. However, distinguishing and identifying various defects during experiments is challenging. In this study, we comprehensively explore point defects and grain boundaries in MoTe2 using first-principles calculations. We simulate the corresponding scanning tunneling microscopy (STM) and transmission electron microscopy (TEM) images to characterize different types of defects. The same type of point defects in the 1T' phase exhibits lower formation energies than those in the 1H phase. The grain boundaries of the 1T' phase form more easily, with corresponding formation energies ranging from 0.07 to 0.14 eV/Å. The partial densities of states indicate that the electronic properties of the 60°, 60°-glide, and 120° grain boundaries (GBs) in the 1T' phase are similar, while various types of defect rings in the 1H phase differ significantly. Our theoretical results effectively reduce the primary cost of characterizing defects and provide essential guidance for experimental references and identifications.

Sanitary ceramic products, such as toilet and wash basin, are widely used in our daily life. Sanitary ceramics are expected to have some excellent physical properties, such as corrosion resistance, easy cleaning, and low water absorption. However, surface defects in sanitary ceramics are inevitable due to complex production processes and changing production environment. Therefore, surface defect detection must be performed in the manufacturing process of sanitary ceramics. There are many types of surface defects in sanitary ceramics, and different types of defects have large differences in characteristics and scales. Traditional detection methods with artificially designed features and classifiers are difficult to apply in this context. In addition, there are few studies on surface defect detection methods of sanitary ceramics based on deep neural networks. In this article, a lightweight real-time defect detection network based on the lightweight backbone MobileNetV3 is presented. The proposed network achieves multi-scale detection of surface defects in sanitary ceramics with a multi-layer feature pyramid. Combining region proposal network and anchor-free method, real-time defect detection is achieved. Finally, a detection head with channel attention structure and a low-level mixed feature classification strategy is used to perform defect classification with higher accuracy. Experimental results show that the proposed approach achieves at least 22.9% detection speed improvement and 35.0% average precision improvement while reducing memory consumption by at least 8.4% compared with the classic one-stage SSD, YOLO V3 and two-stage Faster R-CNN methods.

Defect detection is an important step in industrial production of monocrystalline silicon. Through the study of deep learning, this work proposes a framework for classifying monocrystalline silicon wafer defects using deep transfer learning (DTL). An existing pre-trained deep learning model was used as the starting point for building a new model. We studied the use of DTL and the potential adaptation of MobileNetV2 that was pre-trained using ImageNet for extracting monocrystalline silicon wafer defect features. This has led to speeding up the training process and to improving performance of the DTL-MobileNetV2 model in detecting and classifying six types of monocrystalline silicon wafer defects (crack, double contrast, hole, microcrack, saw-mark and stain). The process of training the DTL-MobileNetV2 model was optimized by relying on the dense block layer and global average pooling (GAP) method which had accelerated the convergence rate and improved generalization of the classification network. The monocrystalline silicon wafer defect classification technique relying on the DTL-MobileNetV2 model achieved the accuracy rate of 98.99% when evaluated against the testing set. This shows that DTL is an effective way of detecting different types of defects in monocrystalline silicon wafers, thus being suitable for minimizing misclassification and maximizing the overall production capacities.

Titania (TiO2) is a widely used semiconductor for the photocatalytic decomposition of organic impurities in air, water and the conversion of CO2 into hydrocarbon fuel precursors. TiO2 in the form of nanotubes arrays is the most attractive for practical use because of the morphological advantages providing more favorable diffusion of photocatalytic reaction products and a low recombination rate of photogenerated electrons and holes. We have carried out a comparative study of the photocatalytic activity of gas-phase conversion of CO2 to hydrocarbon products and the defect properties of multi-walled and single-walled arrays of TiO2 nanotubes. Methanol and methane have been detected in the CO2 photoreduction process. The photocatalytic evolution rate of multi-walled TiO2 nanotubes is twice as fast for methane as for single-walled TiO2 nanotubes after four hours of irradiation and four times faster for methanol. The type and features of the structural defects have been investigated by EPR spectroscopy. For the first time, it has been shown that Ti3+/oxygen vacancy centers are mainly located inside the outer layer of nanotubes, while carbon dangling bonds have been observed directly on the surface of the inner layer. Carbon defects have been found to be the centers of adsorption and accumulation of photoinduced charge carriers. The results are entirely new; they clarify the role of different types of defects in the photocatalytic conversion of CO2 to hydrocarbon compounds and show good prospects for applying TiO2 nanotube arrays.

This paper presents a statement of the works performed in L.E.M.T.A by the members of the thermal and mechanical heterogeneous media research group during the last six years concerning the solving of coupled conductive and radiative heat transfers within a multilayer and semi-transparent “wall”. Out of the authors, this paper allows to take inspiration from the works of D. Maillet, M. Lazard and V. Manias[19, 20, 21]. The aim of these works is to represent in a macroscopic way, with the minimum number of thermophysical parameters, the heat transfers in a plane system composed of semi-transparent media. The approach we propose is semi-analytic (Kernel substitution technique, Laplace transformation) and allow to obtain in the Laplace domain an analytical solution that can be easily used. This method can be applied in two main scopes of applications: the estimation of thermophysical properties (phononic conductivity, optical thickness, Planck number for instance) of semi-transparent materials (glasses, crystals, glass wool, semi-conductors, synthetic diamonds, vitroceramics and so on) and the modelling of processes with semitransparent walls (for instance bottles forming, flat glass production, drying of paper). The method will be first presented and validated and two examples of applications will be then given. This method can be applied to semitransparent walls that emit, absorb and scatter the radiant energy (participating medium). It appears from the principle of a Kernel substitution technique applied to the radiative flux expression and initially introduced by Lick[1] that allows to change the character of the governing heat equation from the integro-differential form to a purely differential one. In the case of limiting cases of purely scattering and purely absorbing media, the solution of the radiative transfer equation is exact. In the general case, we make a two-flux approximation. In all cases, we assume a linear transfer and use the Laplace transform. The method can be applied to grey or grey by bands media, with isotropic or anisotropic scattering. The advantage of the method is fast computational times for good precision.