Simulation and experimental research on magnetic detection technology for closed crack defects of superalloys

Abstract

Closed cracks in superalloy components are characterized by their small scale, concealment, and irregularity, presenting significant challenges for accurate non-destructive testing (NDT). This study introduces magnetic detection technology and demonstrates its efficacy in detecting closed cracks through numerical simulations, model experiments, and a particle swarm optimization-support vector machine (PSO-SVM) classification method. Simulations were conducted to investigate the magnetic response characteristics of closed cracks with varying depths and sizes. Subsequently, the influence of excitation frequency and probe scanning direction on defect responses was analyzed. Using a custom-designed magnetic detection instrument, artificial groove defects in superalloys were tested, and a classification framework for closed crack defects was established. The results reveal a linear correlation between the abnormal signal amplitude and defect parameters, including depth, size, excitation frequency, and scanning direction. Experimental validation confirmed the effectiveness of the magnetic detection technology for identifying closed cracks with burial depths less than 5 mm. Additionally, the optimized PSO-SVM classification method achieved an average accuracy of 98.11 % for defect classification and 92 % for quantification. These findings provide critical theoretical and technical insights into the detection of closed cracks in superalloys.