# Recent Advances on Machine Learning Applications in
## 3 Discussion
This is why, for example, both vibrations and AE sensors tend to be used together in order to simultaneously consider both high- and low-frequency components. Multi-sensor applications tend to have a wider application and better results, although the need for correct feature extraction and selection methods is a necessity to allow ML and DL applications with higher amounts of data. Interestingly, the current sensors are getting more attention for TCM, as they tend to be able to correctly represent the wear stages; these applications tend to need an extra sensor to recognize the actual state, as the current values depend on the machining parameters. The current sensors tend to be subjected to considerable noise, and the actual signal influenced by the damping and friction between elements; nonetheless, it has a limited sensitivity band with respect to other sensors, such as AE. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Application** & **Machine** & **Algorithm** & **Input Parameters** & **Feature Extraction** & **Feature Selection** & **Results** & **Ref.** \\ \hline Tool Health Indicator & & & & & & & \\ Estimation for Blade Wear Monitoring & & & & & & & \\ \hline Tool Wear Regression & & & & & & & \\ \hline Tool Wear Perception & & & & & & & \\ \hline Tool Wear Curve Prediction & & & & & & & \\ in Drilling of CFRP & & & & & & & \\ \hline Tool Wear Classification & & & & & & & \\ \hline Tool Wear Classification & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Comparisons of the different ML methods applied in machining for TCM. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Application** & **Machining** & **Algorithm** & **Input Parameters** & **Feature Extraction** & **Feature Selection** & **Results** & **Ref.** \\ \hline Tool Wear Condition & Milling & TA-KELM & Current & TD, FD, and WPT & - & RMSE = 0.0328 mm & [106] \\ Monitoring & Milling & TA-KELM & Vibrations, Forces, Current, Sound & TD, FD, and WPT & BDE & RMSE = 0.0013 mm & [107] \\ \hline Tool Wear Regression & Milling & ANN & Sound & WPT & VIF & 8.59\% & [109] \\ Monitoring & & & & & & \\ \hline Tool Wear Amount & Milling & RF & Vibrations, AE, and Forces & TD & - & MSE = 10.156 μm2 & [110] \\ \hline Tool Wear Amount & & & & & & & \\ Prediction Under Varying & Milling & FCNN (MAML) & Forces, Current, and Power & - & \begin{tabular}{c} Deep-FS, and \\ Entropy Weight-Grey \\ Correlation Analysis \\ \end{tabular} & MAE = 0.02 mm & [114] \\ Cutting Conditions & Milling & CHMM & Forces and Torque & TD, WPT & FDR & -92\% & [122] \\ \hline Tool Condition Classification & Milling & ANN & Forces, Current, and Voltage & TD & \
\begin{tabular}{c} Cross-Correlation \\ Chart \\ \end{tabular} & 0.031 mm & [123] \\ \hline In-process Tool Wear & Milling & ANN & Forces, and Process & TD & - & \(\pm\)0.037 mm & [124] \\ Prediction & Milling & ANN & Parameters & - & - & 2\% & [125] \\ \hline Tool Wear Regression & Milling & ANN & Process Parameters & - & - & 2\% & [125] \\ Prediction & & & & & & \\ \hline \end{tabular}
\begin{tabular}{c c c c c c} \hline Vibrations, AE, & & & & & & \\ Forces, Spindle & LSTM & - & RMSE = 0.0456 mm & [126] \\ Parameters & & & & & & \\ Condition Classification & Milling & GWO-SVM & Vibrations OR Forces & TD, FD, WPT & GA & \textgreater{}96\% & [127] \\ \hline Tool Wear Classification & Milling & OS-ELM & Current & SDAE & - & 96.84\% & [128] \\ \hline Tool Wear Amount & Milling & Parallel RF & Vibrations, AE, and Forces & TD & - & MSE = 10.469 μm2 & [129] \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Application** & **Machining** & **Algorithm** & **Input Parameters** & **Feature Extraction** & **Feature Selection** & **Results** & **Ref.