Magneto-optical imaging feature extraction of micro-gap weld joint under nonuniform magnetic field excitation.

To reduce the effect of the nonuniformity of magnetic field excitation on micro-gap weld joint magneto-optical (MO) imaging, a new experimental system based on the Faraday MO effect to detect micro-gap welds (gap width less than 0.1 mm) under nonuniform magnetic field excitation was developed. Horseshoe permanent magnets were used to magnetize the weldment and establish a nonuniform magnetic field at the welding joint. MO images of the micro-gap weld joint were captured using an MO sensor under nonuniform magnetic field excitation. After analyzing the distribution characteristics of the magnetic induction intensity in the weld joint area, a characterization method for the weld zone slope was proposed. The weld zone slope could accurately determine the MO imaging effects under the nonuniform magnetic field. A model based on an error backpropagation (BP) neural network was used to predict the offset of the weld joint center at each moment, and the results performed by BP were utilized to optimize the measured value of the weld joint center. Experimental results show that it can accurately extract the position of micro-gap welds under nonuniform magnetic field excitation.

Modeling for detecting weld defects based on magneto-optical imaging.

A magneto-optical (MO) imaging nondestructive testing (NDT) method for ferromagnetic weldments has been proposed. The mechanism of MO imaging was analyzed by the Faraday MO effect, magnetic domain theory, and magnetic hysteresis loops. Then, the relation between MO images and their corresponding excitation voltages was investigated. To explain the MO imaging system, magnetic domain distribution models of various welding states were established. These models are excited by two kinds of magnetic fields. One is the external magnetic field (Hex), and the other is a weldment remanence field (Mr) after Hex is removed. Relations of magnetic field excitation voltages, thickness of the spacer plate, and the corresponding MO images were also researched, which indicates the proposed NDT method can be used to detect incomplete penetration defect. Then, an experiment that uses MO imaging to detect the defects of high-strength steel (HSS) weldment was performed. Experimental results proved this method can detect crack, sag, and incomplete penetration of weldment effectively. Finally, a series of welded joint MO images of the HSS weldment were captured, which are used as the input data of the defect classification model established by using principal component analysis and an error backpropagation neural network, and the accuracy of this classification model can achieve 92.8%.