APPLICATION OF ARTIFICIAL NEURAL NETWORKS TO EVALUATE WELD DEFECTS OF NUCLEAR COMPONENTS
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Artificial neural networks (ANNs) are computational representations based on the biological neural architecture of the brain. ANNs have been successfully applied to a wide range of engineering and scientific applications, such as signal, image processing and data analysis. Although Radiographic testing is widely used for welding defects, it is unsuccessful in identifying some welding defects because of the nature of image formation and quality. Neoteric algorithms have been used for the purpose of weld defects identifications in radiographic images to replace the expert knowledge. The application of artificial neural networks in noise detection of radiographic films is used. Radial Basis (RB) and learning vector quantization (LVQ) were applied. The method shows good performance in weld defects recognition and classification problems.Keywords:
Learning vector quantization
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Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation R. Sikora, T. Chady, P. Baniukiewicz, M. Caryk, B. Piekarczyk; THE CHOICE OF OPTIMAL STRUCTURE OF ARTIFICIAL NEURAL NETWORK CLASSIFIER INTENDED FOR CLASSIFICATION OF WELDING FLAWS. AIP Conference Proceedings 22 February 2010; 1211 (1): 631–638. https://doi.org/10.1063/1.3362453 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
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As an important part of non-destructive testing, infrared thermography testing is widely used in various fields of industrial development for monitoring the quality of metal parts. Considering the problem of low detection rate of surface defects on steel parts, we explored the application of neural architecture search (NAS) in infrared thermography area for the first time. On the one hand, we compared different time-series temperature features of defect locations in infrared images and validate the performance of three different features such as heating, cooling and full process by machine learning methods. On the other hand, we searched for multilayer perceptron through NAS technology to classify defects with different depths. Experiments have proved that the time-series temperature feature is very effective when used in the depth classification of defects, and the accuracy rate can reach 93% under the verification of traditional machine learning methods. The NAS technique used in this paper can search 100 multilayer perceptrons in a minimum of 121s and achieve 100% defect classification accuracy.
Thermography
Perceptron
Feature (linguistics)
Multilayer perceptron
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这份报纸基于人工的神经网络论述了一个联机优秀检查系统。色差分类和边察觉是在玻璃钢表面质量检查的二个困难的问题。二个人工的神经网络被做，二个问题被解决。一个解决的色差分类。色彩，浸透和他们三的概率渲染，谁的出现可能性在颜色直方图是最大的，当输出节点的输入参数，和数字能与要求的变化被调整，被选择。另外的解决的边察觉。在这个中立网络，灰色的规模图象的边察觉能为一幅二进制图象与训练神经网络被测试。它如果灰色的规模图象直接被认为是训练样品，阻止训练样品需要的数字太大的困难。这个系统能被用于玻璃钢差错检查而且另外的产品不仅联机优秀检查和分类。
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An innovative neruofuzzy network is proposed herein for parameter recognition, specifically for steel plate's defects size inspection through different NDT sources data fusion. A neural network architecture is used to automatically deduce membership function based on a hybrid supervised learning scheme and a set of activation functions are used to adapt to different fuzzy states. The realization of this model and its characteristics are discussed in detail. The application of this model on the inspection of surface defect sizes shows that a quantitative method for determining the actual defect size is successfully developed to make full use of the measured defects sizes from different NDT sources.
Realization (probability)
Activation function
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The characteristics of bearing vibration can be used to detect common bearing defect, like noise defect etc. This method unfortunately can not detect the visual defects on the inner and outer ring bearing surface. A pattern recognition is implemented in this paper to solve the problem. A backpropagation neural network architecture is used to recognize the visual defect pattern. This architecture is integrated in a digital image processing chain. Recognition rate of good bearing is obtained at 92.93 %, meanwhile for defected bearing is obtained at 75 % respectively. This rate shows integrated artificial neural network with digital image processing can be implemented to detect the presence of visual bearing defect.
Backpropagation
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