Feature Extraction of Sectorial Scan Image of Thick-Walled Electron Beam Welding Seam Based on Principal Component Analysis

A feature extraction method was proposed to sectorial scan image of Ti-6Al-4V electron beam welding seam based on principal component analysis to solve problem of high-dimensional data resulting in time-consuming in defect recognition. Seven features were extracted from the image and represented 87.3 % information of the original data. Both the extracted features and the original data were used to train support vector machine model to assess the feature extraction performance in two aspects: recognition accuracy and training time. The results show that using the extracted features the recognition accuracy of pore, crack, lack of fusion and lack of penetration are 93%, 90.7%, 94.7% and 89.3%, respectively, which is slightly higher than those using the original data. The training time of the models using the extracted features is extremely reduced comparing with those using the original data.