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. 
Force based tool wear monitoring system for milling process based on relevance vector machine