Flaw Identification of Metal Material in Eddy Current Testing Using Neural Network Optimized by Particle Swarm Optimization
نویسندگان
چکیده
As an NDT technology, Eddy current testing is widely used to identify the surface flaw of metal material. However, due to the complex relationship between the test results and the flaw’s shape, the identification is qualitative in most situations. In the paper, a neural network optimized by particle swarm optimization (PSO) is used to quantify the detection result tentatively of the fault on the subsurface of the metal material. Here, PSO is used to optimize the weight value and the threshold value of the BP neural network. According to the experiment, forecasting geometric dimension of the flaws in conductor by PSO-optimized neural network is relatively ideal.
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