关键词: 高温合金, 弱磁检测, 缺陷反演, 参数优化, 支持向量机

Abstract: The quantitative method of magnetic anomaly of superalloy surface defects based on the parameter optimization of SVM was of great significance to solve the engineering problems of surface crack detection for turbine disks. A sample database was constructed with 16 preset magnetic anomaly characteristic values of defects obtained by weak magnetic detection, a SVM prediction model was established to invert and quantify of defect sizes. The validity of parameter optimization and the SVM model were verified by the specimens with known defects. The results show that after parameter optimization, the prediction results of length, width and depth are improved compared with the prediction results of default parameters, especially the inversion effectiveness of length and depth are significantly improved, and the prediction accuracy of genetic algorithm is better than that of cross-validation method. When the magnetic permeability of the defect and the base material is very different, such as ferromagnetic inclusions on the surfaces of the superalloy, the characteristic value amplitude of the magnetic anomaly is more larger. After the characteristic value amplitude and area are halved, the accuracy of the results is improved by more than 20%. The SVM model with genetic algorithm parameter optimization still shows good predictive ability for data outside the sample database, and the prediction accuracy is close to 85%.

Key words: superalloy, weak magnetic detection, defect inversion, parameter optimization, support vector machine(SVM)