期刊
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
卷 92, 期 5-8, 页码 2853-2865出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00170-017-0367-1
关键词
Tool wear monitoring; Milling force signals; Correlation analysis; GMHMM; BPNN
资金
- 863 National High-Tech Research and Development Program of China [2013AA041108]
Tool wear monitoring system is of vital importance for the guarantee of surface integrity and manufacturing effectiveness. To overcome the weaknesses of neural networks, a new tool wear estimation model based on Gaussian mixture hidden Markov models (GMHMM) is presented. Nine types of time-domain features are extracted from the milling force signals which are obtained under four sorts of tool wear state. Besides, the sensitive features which can indicate the tool wear states accurately are selected out by correlation analysis. To test the effectiveness of the presented model, the selected sensitive features serve to identify the tool wear states by utilizing GMHMM and back-propagation neural network (BPNN), respectively. Moreover, the identification performance of GMHMM under the combinations of various numbers of Gaussian mixtures and various lengths of observation sequence is analyzed to verify the practicability of the presented tool wear model. The experimental results show that the GMHMM-based model can identify the tool wear states effectively and GMHMM outperforms the BPNN model in accuracy and stability. This method lays the foundation on tool wear monitoring in real industrial settings.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据