Quantitative NDT of Metallic Foam Based on Direct Current Potential Drop
-
摘要: 提出一种基于直流电位法的泡沫金属定量无损检测方法。该方法采用主元分析前馈神经网络求解逆问题，对内部缺陷进行定量。正问题求解采用有限元方法。为解决三维直流电场分析中庞大的节点数及神经网络所需大量训练数据带来的计算资源问题，开发了一种基于知识库的快速直流电位分布计算方法。算例结果表明，神经网络可有效用于泡沫金属的定量无损检测；所提出的快速算法可大大缩短计算时间且具有较高计算精度。Abstract: A numerical method of quantitative nondestructive testing(NDT) of metallic foam was developed on the basis of the direct current potential drop(DCPD). A feed forward neural network(NN) was applied for the inverse mapping to quantify the inner flaw in addition with a principal component analysis process. Solving of obverse problem utilizes finite element method(FEM). To cope with the huge computer burden necessary to generate training data for NN of three dimensions of giant nodes in DC field, a novel fast forward solver based on databases was proposed for the rapid computation of DCPD signals. The numerical results indicated that the quantitative NDT of metallic foam can be well performed by using the NN approach and the proposed fast solver can significantly decrease the computer resources but with a satisfactory accuracy.
-
Keywords:
- Metallic foam /
- Quantitative nondestructive testing /
- Neural networks /
- Fast solver
-
-
[1] 卢天健，何德坪,陈常青等.超轻多孔金属材料的多功能特性及应用\[J\].力学进展,2006,36(4):517-518. [2] 李家伟，陈积懋.无损检测手册\[M\].北京：机械工业出版社,2002:2-4;426. [3] 解社娟，陈振茂,张东利，等.泡沫金属直流电位无损检测方法研究\[J\].南昌航空大学学报：自然科学版,2007,21(增刊):188-193. [4] 刘培生，李铁藩,付超，等.泡沫金属电阻率的计算方法\[J\].稀有金属材料与工程,1999,28(4):260-263. [5] 盛剑霓.工程电磁场数值分析\[M\].西安：西安交通大学出版社,1999. [6] Zhenmao Chen, Kenzo Miya. ECT inversion using a knowledge-based forward solver\[J\]. Journal of Nondestructive Evaluation,1998,17(3):167-175. [7] Ghajarieh R, Saka M, et al. Simplified NDE of multiple cracks by means of the potential drop technique\[J\]. NDT & E International,1994,28(1):23-28. [8] Nicola Bowler. Theory of four-point alternating current potential drop measurements on a metal half-space\[J\]. Applied Physics,2005,39(3):584-589. [9] Radu C. Popa, Kenzo Miya. Approximate inverse mapping in ECT, based on aperture shifting and neural network regression\[J\]. Journal of Nondestructive Evaluation,1998,17(4):209-221. [10] Zhenmao Chen. Sizing and classification of defects in SG tubes of a nuclear power plant from remote field ECT signals by using neural networks\[C\]. Proceedings of ICEF′, Chongqing:2008. [11] Yusa N, Chen Z, Miya K. Quantitative profile evaluation of natural cracks in a steam generator tube from eddy current signals\[J\]. International Journal of Applied Electromagnetics and Mechanics,2000,12(3):139-150.
计量
- 文章访问数: 0
- HTML全文浏览量: 0
- PDF下载量: 1
下载:
