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 timeconsuming in de...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 timeconsuming 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.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant Nos.51575134 and 51205083)
文摘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 timeconsuming 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.
