Magnetic flux leakage quantitative identification of micro crack based on GA-BP neural network

As the fact that the crack sizes identified based on magnetic flux leakage are larger than 1 mm generally, which are far different from the natural cracks in macro-crack check area. An algorithm with GA-BP neural network was investigated to detect quantificationally the rectangular micro-cracks with less than 0.50 mm width and depth. And a database was developed for micro crack defects among 0.10~0.30 mm based on theoretic calculation of the magnetic dipole model and experiment of magnetic flux leakage. Results show that, due to the noises interference existing in the actual detection process, the prediction error of the experimental data is larger than that of the theoretical data, and the maximum can reach 16.73%, but the prediction results can basically reflect the size of the micro cracks.