## 5 Conclusions  
In this paper, a cluster-based tool condition monitoring (TCM) system is established and validated. The proposition of a novel clustering method based on adjacent grids searching (CAGS) and a tool condition monitor based on cluster density factor are the key aspects. The main conclusions of this work are listed as follows. 1. The proposed TCM system performs successfully under variable cutting parameters and even variable cutting processes such as milling and turning. The prediction accuracy of our system for Test I, Test II (A, B, C) and Test III is 94%, 100%, 75%, 75% and 100%, respectively. In contrast, the highest prediction accuracy of BP neural network, Bayesian network and SVM for Test II (A, B, C) is 83%, 75% and 72%, respectively. Specifically, in the TCM of milling process under variable cutting parameters (Test II), our system presents robustness when the dataset is fed in total into the TCM system. This capability is required in the case of unknown cutting parameters. 2. An important statistical correlation between tool wear and the distribution of cutting force features is revealed to find robust information indicating tool wear conditions. Since the cluster density is dimensionless, it will not be affected by changes in the magnitude of the cutting force caused by variable cutting parameters. Moreover, only a common time-domain feature extraction is needed rather than frequency-domain analysis or even time-frequency analysis. 3. A novel clustering method CAGS is proposed to realize unsupervised monitoring. Most unsupervised methods are difficult to use in TCM since the output labels could not match real-world information. However, in addition to randomly generated cluster labels, CAGS also outputs further intrinsic information of the dataset such as cluster density. This intrinsic information could be used to indicate the real-world properties of each cluster. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Training Dataset**} & \multicolumn{3}{c}{**Test Dataset**} \\ \cline{2-4}  & **A** & **B** & **C** \\ \hline A & - & 67\% & 63\% \\ B & 83\% & - & 72\% \\ C & 65\% & 66\% & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Prediction accuracy of Bayesian network on Test II. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Training Dataset**} & \multicolumn{3}{c}{**Test Dataset**} \\ \cline{2-4}  & **A** & **B** & **C** \\ \hline A & - & 75\% & 50\% \\ B & 76\% & - & 50\% \\ C & 75\% & 0\% & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Prediction accuracy of SVM on Test II. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Training Dataset**} & \multicolumn{3}{c}{**Test Dataset**} \\ \cline{2-4}  & **A** & **B** & **C** \\ \hline A & - & 75\% & 50\% \\ B & 76\% & - & 50\% \\ C & 75\% & 0\% & - \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Prediction accuracy of proposed method on Test II. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Training Dataset**} & \multicolumn{3}{c}{**Test Dataset**} \\ \cline{2-4}  & **A** & **B** & **C** \\ \hline A & - & 67\% & 63\% \\ B & 83\% & - & 72\% \\ C & 65\% & 66\% & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Prediction accuracy of BP network on Test II. * A cluster density factor is proposed to accomplish the TCM process, which can convert the cluster density into the value reflecting actual tool wear. Experimental results indicate that the factor is easy to obtain by using a stationary truncation coefficient \(T\), which is set to 5.54 in all tests in this paper. This factor can be widely used in variable cutting processes. * From the perspective of experimental design, a full test set is conducted. A whole life-cycle wear experiment under the same milling condition is firstly carried out to simulate the TCM of cutting process of a large-scale workpiece. A tool wear experiment under variable milling conditions is then performed to verify the effectiveness of our method for machining a complex part with multiple operations.