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Inversion of thicknesses of multi-layered structures from eddy current testing measurements 被引量:1
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作者 黄平捷 吴昭同 《Journal of Zhejiang University Science》 EI CSCD 2004年第1期86-91,共6页
Luquire et al. ' s impedance change model of a rectangular cross section probe coil above a structure with an arbitrary number of parallel layers was used to study the principle of measuring thicknesses of multi-l... Luquire et al. ' s impedance change model of a rectangular cross section probe coil above a structure with an arbitrary number of parallel layers was used to study the principle of measuring thicknesses of multi-layered structures in terms of eddy current testing voltage measurements. An experimental system for multi-layered thickness measurement was developed and several fitting models to formulate the relationships between detected impedance/voltage measurements and thickness are put forward using least square method. The determination of multi-layered thicknesses was investigated after inversing the voltage outputs of the detecting system. The best fitting and inversion models are presented. 展开更多
关键词 Multi layered structure Thickness measurement Eddy current testing Multi frequency INVERSION
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Neural network approach for modification and fitting of digitized data in reverse engineering~
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作者 鞠华 王文 +1 位作者 谢金 陈子辰 《Journal of Zhejiang University Science》 EI CSCD 2004年第1期75-80,共6页
Reverse engineering in the manufacturing field is a process in which the digitized data are obtained from an existing object model or a part of it, and then the CAD model is reconstructed. This paper presents an RBF n... Reverse engineering in the manufacturing field is a process in which the digitized data are obtained from an existing object model or a part of it, and then the CAD model is reconstructed. This paper presents an RBF neural network approach to modify and fit the digitized data. The centers for the RBF are selected by using the orthogonal least squares learning algorithm. A mathematically known surface is used for generating a number of samples for training the networks. The trained networks then generated a number of new points which were compared with the calculating points from the equations. Moreover, a series of practice digitizing curves are used to test the approach. The results showed that this approach is effective in modifying and fitting digitized data and generating data points to reconstruct the surface model. 展开更多
关键词 Reverse engineering Digitized data Neural network modification and fitting
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