基于数据增广灰色神经网络的 ACFM 裂纹角度预测.

Alternating current field measurement (ACFM) technology is widely used in defect detection of metal structures in manufacturing and other industrial fields. The angle deflection and crack location problems of single sensor in the process of unpredictable defect detection were studied. Firstly, through the COMSOL Multiphysics simulation results, it can be seen that the X and Y direction components of the field strength have a law of signal complementarity during the angular deflection process, and then the data augmented grey neural network model (DA-GNNM) prediction was realized by establishing a scale factor. At the same time, simulation prediction and regression prediction showed that DA-GNNM model has better prediction effect. In addition, the deflection crack was reconstructed by multigradient deflection simulation. Secondly, the DA-GNNM prediction model was verified to be reasonable by building an experimental platform and signal feature extraction, with an average prediction error of 2. 56% . Finally, the deflection of the reconstructed image of the crack in the non-parallel detection process is further improved through the prediction angle. [ABSTRACT FROM AUTHOR]