- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Effectiveness of phased array focused ultrasound and active infrared thermography methods as a nondestructive testing of Ni-WC coating adhesion
摘要： The substrate/coating adhesion is a crucial parameter conditioning the quality of coating and its durability in service. For this reason, an inspection of the coating integrity, in particular, the presence of adhesion defects will be of great importance. The adhesion inspection is usually ensured by destructive methods, such as traction, interfacial indentation, four-point bending, testing scratch, etc. However, it is currently hampered by the absence of a satisfactory non-destructive method. Among the non-destructive testing technologies widely used in the industrial field, there are X-ray diffraction, ultrasonic inspection, and infrared thermography. In this paper, two methods are investigated: ultrasonic inspection, which becoming more efficient, especially with the emergence of phased array systems that allow to investigate different inspection angles and focusing depths, and the active infrared thermography. Experiments were performed on metallic coatings deposited on a mild steel substrate. Coatings were containing artificial defects (flat bottom holes with different diameters) at the interface and others were exempts of defects. Longitudinal waves with specific delay laws were generated through a phased array contact transducer (5 MHz of central frequency). Experimental results show that the ultrasonic method allows detecting and sizing defects with a diameter of 1 mm located in thick coatings.
关键词： Substrate/coating adhesion,phased array,defects,detection,nondestructive testing,coating
更新于2025-09-23 15:22:29
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Detection of surface defects on solar cells by fusing Multi-channel convolution neural networks
摘要： Manufacturing process defects or artificial operation mistakes may lead to solar cells having surface cracks, over welding, black edges, unsoldered areas, and other minor defects on their surfaces. These defects will reduce the efficiency of solar cells or even make them completely useless. In this paper, a detection algorithm of surface defects on solar cells is proposed by fusing multi-channel convolution neural networks. The detection results from two different convolution neural networks, i.e., Faster R-CNN and R-FCN, are combined to improve detection precision and position accuracy. In addition, according to the inherent characteristics of the surface defects in solar cells, two other strategies are used to further improve the detection performance. First, the anchor points of the region proposal network (RPN) are set by adding multi-scale and multi-aspect regions to overcome the problem of high false negative rate caused by the limitation of anchor points. Second, in view of the subtle and concealed defects of solar cells, the hard negative sample mining strategy is used to solve the problem of low detection precision caused by the negative sample space being too large. The experimental results showed that the proposed method effectively reduced the false negative rate and the false positive rate of a single network, and it greatly improved the accuracy of the locations of defects while improving the object recall rate.
关键词： Deep learning,Defects detection,Faster R-CNN,Solar cell,R-FCN
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE International Conference on Big Knowledge (ICBK) - Beijing, China (2019.11.10-2019.11.11)] 2019 IEEE International Conference on Big Knowledge (ICBK) - U-Net Based Defects Inspection in Photovoltaic Electroluminecscence Images
摘要： Efficient defects segmentation from photovoltaic (PV) electroluminescence (EL) images is a crucial process due to the random inhomogeneous background and unbalanced crack non-crack pixel distribution. The automatic defect inspection of solar cells greatly influences the quality of photovoltaic cells, so it is necessary to examine defects efficiently and accurately. In this paper we propose a novel end to end deep learning-based architecture for defects segmentation. In the proposed architecture we introduce a novel global attention to extract rich context information. Further, we modified the U-net by adding dilated convolution at both encoder and decoder side with skip connections from early layers to later layers at encoder side. Then the proposed global attention is incorporated into the modified U-net. The model is trained and tested on Photovoltaic electroluminescence 512x512 images dataset and the results are recorded using mean Intersection over union (IOU). In experiments, we reported the results and made comparison between the proposed model and other state of the art methods. The mean IOU of proposed method is 0.6477 with pixel accuracy 0.9738 which is better than the state-of-the-art methods. We demonstrate that the proposed method can give effective results with smaller dataset and is computationally efficient.
关键词： cracks detection,electroluminescence images,U-net,Solar cell defects detection
更新于2025-09-16 10:30:52
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3D laser scanning for monitoring the quality of surface in agricultural sector
摘要： The paper considers three technologies for obtaining data on the road surface - through video recording, thermal imaging and laser scanning for the purpose of monitoring, diagnostics and control of the road quality. An analysis of the first two methods showed their significant drawbacks, such as the inability to measure the geometric parameters of deformations (video recording) and the significant dependence of the measurement results on external conditions (thermal imaging). Laser scanning, on the contrary, has a number of advantages, including coordinate referencing, obtaining a three-dimensional model, its transformation and measurement of parameters. Laser scanning is widely used, but mainly for measuring the quantitative characteristics of objects. The paper discusses the application of the laser scanning method to determine the qualitative characteristics of the road surface - the presence or absence of defects, which include hollow spots, waves, cavities, chipping, bleeding, humps, cracks, vertical displacement of road plates, rutting, unevenness of patching, damage to the road surface, track, breach, destruction of the pavement edge, subsidence followed by a complex of repair work. For this, a ground-based laser scanning was performed, the results of which were processed using the Leica Cyclone 9.4 software. According to the scanning data, defects were detected in the form of soil subsidence, hollow spots and humps. The performed work revealed a drawback of the laser scanning method, which consists in the absence of automated detection and recognition of deformations. A number of measures have been proposed to improve this drawback, which slows down the randomness and quality of work in monitoring and diagnosing the road. Further prospects for research on this topic, in particular the multi-purpose use of scanning data, by creating a distributed ledger are also indicated.
关键词： agricultural sector,3D laser scanning,road surface quality,monitoring,defects detection
更新于2025-09-16 10:30:52