## 5 Conclusions  
In this paper, clustering online monitoring based on the time-frequency intrinsic characteristics of acoustic emission and cluster density factor is proposed that realizes the unsupervised online monitoring of tool wear in corner milling. The results show that the cluster density factor could effectively characterize the real tool wear values. The following conclusions can be summarized:  
1. In order to study and analyze the complexity of the corner milling of aviation structural parts and its influence on multi-sensor signals, a metal cutting condition monitoring platform based on multi-sensor information synchronous acquisition is built.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Recognition Algorithm** & **PUR** & **CSM** & **NMI** & **CluCE** & **ClaCE** \\ \hline CAGS & 0.58 & 0.64 & 0.43 & 0.61 & 0.45 \\ BP network & **0.70** & **0.71** & **0.48** & **0.53** & 0.52 \\ Bayesian network & 0.51 & 0.54 & 0.30 & 0.75 & **0.43** \\ SVM & _0.48_ & _0.47_ & _0.21_ & _0.80_ & _0.62_ \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of the wear identification accuracy of the peripheral corner milling tool. (The highest score of each index is marked in bold, and the lowest score is marked in italics).  
Figure 15: Comparison of _War_ and tool wear value from variation tendency. (**a**) Change trend of _War_ and VB; (**b**) Change trend of _War_ and \(VB_{max}\).  
A verification experiment of tool wear condition monitoring for corner peripheral milling is designed.
2. A method of tool wear-sensitive feature extraction based on the inherent features of time-frequency images is proposed. Unsupervised feature selection based on trend item analysis are used to obtain the best feature set. The effectiveness of the extracted sensitive features is verified.
3. The evolution law of sensitive characteristics with tool wear is analyzed. A tool wear monitoring method using the CAGS-based density factor is proposed. Compared with the supervised algorithms, the results show that the unsupervised monitoring system proposed in this paper has considerable recognition accuracy. However, in terms of robustness, our method has incomparable advantages over the supervised monitoring algorithms.  
In brief, the unsupervised method based on CAGS is an effective solution for tool wear monitoring in the corner-milling process. However, the recognition accuracy and the robustness of the monitoring system need to be improved in further studies.  
[PERSON] performed the data analyses, wrote the manuscript and final approval of the version to be published. [PERSON], [PERSON], M.Y., [PERSON] and [PERSON] helped perform the analysis with constructive discussions and final approval of the version to be published. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Tianjin enterprise science and Technology Commissioner project (21 YDTJPJC00660), the Scientific research project of Tianjin Education Commission (2019 KJ098), the Key projects of Tianjin Natural Science Foundation (16 CZDJC38600) and the Tianjin enterprise science and Technology Commissioner project (20 YDTJPJC00610).  
Not applicable.  
We confirm that neither the manuscript nor any parts of its content are currently under consideration or published in another journal. All authors have approved the manuscript and agree with its submission to Machines.