期刊文献+
共找到2篇文章
< 1 >
每页显示 20 50 100
Study on Acoustic Emission Characteristics of Deformation Damage Process of Zirconia Ceramics
1
作者 Qingchuan Fu yushu lai 《Journal of Materials Science and Chemical Engineering》 2024年第2期61-72,共12页
Zirconia ceramics have become increasingly widely used in recent years and are favored by relevant enterprises. From the traditional dental field to aerospace, parts manufacturing has been used, but there is limited r... Zirconia ceramics have become increasingly widely used in recent years and are favored by relevant enterprises. From the traditional dental field to aerospace, parts manufacturing has been used, but there is limited research on the deformation and damage process of zirconia ceramics. This article analyzes the acoustic emission characteristics of each stage of ceramic damage from the perspective of acoustic emission, and explores its deformation process characteristics from multiple perspectives such as time domain, frequency, and EWT modal analysis. It is concluded that zirconia ceramics exhibit higher brittleness and acoustic emission strength than alumina ceramics, and when approaching the fracture, it tends to generate lower frequency acoustic emission signals. 展开更多
关键词 Zirconia Ceramics Acoustic Emission Monitoring Crack Damage
下载PDF
Research on Electromagnetic Acoustic Emission Signal Recognition Based on Local Mean Decomposition and Least Squares Support Vector Machine
2
作者 Chenglong Yang yushu lai Qiuyue Li 《Journal of Computer and Communications》 2023年第5期70-83,共14页
Electromagnetic acoustic emission technology is one of nondestructive testing, which can be used for defect detection of metal specimens. In this study, round and cracked metal specimens, round metal specimens, and in... Electromagnetic acoustic emission technology is one of nondestructive testing, which can be used for defect detection of metal specimens. In this study, round and cracked metal specimens, round metal specimens, and intact metal specimens were prepared. And the electromagnetic acoustic emission signals of the three specimens were collected. In addition, the local mean decomposition(LMD), Autoregressive model(AR model) and least squares support vector machine (LSSVM) algorithms were combined to identify the eletromagnetic acoustic emission signals of round and cracked, round, and intact specimens. According to the algorithm recognition results, the recognition accuracy of can reach above 97.5%, which has a higher recognition rate compared with SVM and BP neural network. The results of the study show that the algorithm is able to identify quickly and accurately crack defect in metal specimens. 展开更多
关键词 Electromagnetic Acoustic Emission Technology LMD LSSVM Defect Detection of Metal Crack
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部