| 基于人工神经网络的钢铁件淬火硬度电磁无损检测 |
| |
| 引用本文： | 贾健明. 基于人工神经网络的钢铁件淬火硬度电磁无损检测[J]. 金属热处理, 2006, 31(9): 69-71 |
| |
| 作者姓名： | 贾健明 |
| |
| 作者单位： | 常州信息职业技术学院,机电工程系,江苏,常州,213164 |
| |
| 基金项目： | 江苏省高校自然科学基金;江苏省高校高新技术产业发展指导性计划项目 |
| |
| 摘 要： | 利用DWY-1型电磁无损检测仪，采用改进的Gram-Schmidt方法优化的RBF（径向基函数网络）人工神经网络，实现了钢铁件淬火硬度的实时在线无损检测。结果表明，淬火硬度的检测精度、网络的收敛速度能满足生产实际的需要。
|
| 关 键 词： | 人工神经网络 在线检测 电磁无损检测 淬火硬度 |
| 文章编号： | 0254-6051（2006）09-0069-03 |
| 收稿时间： | 2006-03-11 |
| 修稿时间： | 2006-03-11 |
Electromagnetic Non-destructive Test for Quenched Hardness of Iron and Steel Parts Based on ANN |
| |
| JIA Jian-ming. Electromagnetic Non-destructive Test for Quenched Hardness of Iron and Steel Parts Based on ANN[J]. Heat Treatment of Metals, 2006, 31(9): 69-71 |
| |
| Authors: | JIA Jian-ming |
| |
| Affiliation: | Department of Mechanical and Electrical Engineering , Changzhou College of Information Technology, Changzhou Jiangsu 213164, China |
| |
| Abstract: | By means of the new type of electromagnetic nondestructive measurement instrument(DWY-1),using the RBF(Radial Basis Function) neural network optimized by modified Gram-Schmidt method,the real-time on line non-destructive detect for hardness of steel parts was realized.The results show that the detect accuracy and the convergence rate of network can meet the requirement of practical production. |
| |
| Keywords: | artificial neural network(ANN) online detecting electromagnetic non-destructive test(EMNDT) quenched hardness |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
