人工神经网络在导电材料测厚中的应用

Wei Zhang, Surong Qu, Li Li, Qinglin Miao, Changyuan Song
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引用次数: 0

摘要

涡流检测(ECT)正在成为一种广泛应用的检测技术,特别是在飞机、电力和核工业中。影响涡流响应的因素很多。从多层导体的ECT信号中确定厚度的逆问题在一定程度上是一个挑战。本研究的目的是介绍一种基于改进的反向传播神经网络(BPNN)的方法,从其ECT信号中识别多层厚度。通过对多个已知厚度不同的试件进行仿真研究和实验验证,验证了该方法对多层厚度测量的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of ANN in the Thickness Measuring of Conductive Materials
Eddy current testing (ECT) is becoming a widely used inspection technique, particularly in the aircraft, power and nuclear industries. Many factors may affect the eddy current response. Inverse problems to determine the thickness from ECT signals of multilayer conductors have been a challenge for a certain degree. The objectives of this study are to introduce a method based on improved Back Propagation neural network (BPNN) to identify the multilayer thickness from their ECT signals. The simulation study and an experimental validation carried out on a number of specimens with different known thickness confirmed the suitability of the proposed approach for multilayer thickness measuring.
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