节点文献
基于神经网络正向模型和蚁群算法的涡流检测自然裂纹形状重构
Reconstruction of Natural Crack Shapes from the ECT Signals by Using an Artificial Neural Network Based Forward Model and Ant Colony Optimization Algorithm
【摘要】 以神经网络作为正向模型,蚁群优化算法作为反演方法,对采集的疲劳裂纹涡流检测(eddy current testing,ECT)信号进行了反演,重构了裂纹形状。研究了算法中参数的不同选择对反演结果的影响。裂纹形状重构结果表明了神经网络正向模型的有效性和蚁群反演算法的可行性。
【Abstract】 The reconstruction of crack shapes from eddy current testing (ECT) signals is realized by using artificial neural network based forward model and ant colony algorithm. The parameters of the algorithm are modified and the results are analysed. The results of crack shapes reconstruction validate both the efficiency of the forward model and the feasibility of the inverse approach.
【关键词】 自然裂纹;
涡流检测;
神经网络;
正向模型;
蚁群算法;
形状重构;
【Key words】 natural crack eddy current testing artificial neural network forward model ant colony algorithm profile reconstruction;
【Key words】 natural crack eddy current testing artificial neural network forward model ant colony algorithm profile reconstruction;
【基金】 广东省工业攻关计划(2006B12401001)资助
- 【文献出处】 科学技术与工程 ,Science Technology and Engineering , 编辑部邮箱 ,2008年06期
- 【分类号】TB553
- 【被引频次】3
- 【下载频次】204