期刊
ADVANCES IN ENGINEERING SOFTWARE
卷 71, 期 -, 页码 46-51出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.advengsoft.2014.02.002
关键词
Tool wear monitoring; Relevance vector machine; Multinomial function; Milling process
类别
资金
- National Natural Science Foundation of China [51175371]
- National Science and Technology Major Projects [2014ZX04012-014]
- Tianjin Science and Technology Support Program [13ZCZDGX04000]
The monitoring of tool wear status is paramount for guaranteeing the workpiece quality and improving the manufacturing efficiency. In some cases, classifier based on small training samples is preferred because of the complex tool wear process and time consuming samples collection process. In this paper, a tool wear monitoring system based on relevance vector machine (RVM) classifier is constructed to realize multi categories classification of tool wear status during milling process. As a Bayesian algorithm alternative to the support vector machine (SVM), RVM has stronger generalization ability under small training samples. Moreover, RVM classifier results in fewer relevance vectors (RVs) compared with SVM classifier. Hence, it can be carried out much faster compared to the SVM. To show the advantages of the RVM classifier, milling experiment of Titanium alloy was carried out and the multi categories classification of tool wear status under different numbers of training samples and test samples are realized by using SVM and RVM classifier respectively. The comparison of SVM with RVM shows that the RVM can get more accurate results under different number of small training samples. Moreover, the speed of classification is faster than SVM. This method casts some new lights on the industrial environment of the tool condition monitoring. (C) 2014 Elsevier Ltd. All rights reserved.
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