# Recent Advances on Machine Learning Applications in
## 3 Discussion
** \\ \hline Tool Wear State Classification & Milling & SVM & Forces & WTMM, and HE & MI & 86.20\% & [130] \\ \hline Tool Condition Binary Classification & Milling & SVM & Forces & TD & PCA & 91.43\% & [131] \\ \hline Tool Typing Monitoring & Milling & SVM & Vibrations & WTMM, and HE & MI & 98.70\% & [132] \\ \hline Tool Wear Classification in 5 & Milling & CNN & Vibration & DWFs, and HEDS & - & 98.70\% & [14] \\ \hline Tool Wear Classification & Milling & SSAE & AE, and Process & MFCC & - & 99.63\% & [17] \\ \hline In-Process Tool Condition & Milling & LSTM + ResNet & Vibrations, AE, and & - & - & RMSE \(<\) 001995 mm & [30] \\ \hline Tool Wear Type and Amount & Milling & CNN + ATWVD & Tool Images & - & - & MAFE + 4.76\% & [31] \\ \hline Tool Wear Type and Amount & Milling & CNN & Tool Images & Sliding Window & - & \begin{tabular}{c} 17.1 um average error \\ reduction (91.5\% \\ accuracy in pixel \\ classification) \\ \end{tabular}  & \ \begin{tabular}{c} 17.1 um average error \\ reduction (91.5\% \\ accuracy in pixel \\ classification) \\ \end{tabular}  &
\begin{tabular}{c} 17. \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 Binary & & & & & & & \\ Classification & Milling & CNN & Sound & STFT & - & 99.50\% & [136] \\ \hline Tool breakage Classification & Milling & CNN & Current & TD & - & 93\% & [137] \\ \hline Tool Anomaly Detection & Milling & CNN-AD & Current & TD, FFT, and WPT & - & 99.12\% & [138] \\ \hline Tool Wear Estimation for Complex Part Milling & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline Tool Wear Prediction & Milling & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline Tool Wear Classification & Milling & SSAE & Currents & OA based on FFT & - & 98.79\% & [141] \\ \hline Current Health and RUL & & & & & & \\ Prediction & & & & & & \\  & & & & & & \\ \hline Tool Wear Amount Prediction, Classification, and RUL Prediction & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline Tool Wear and RUL & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline Tool Wear Monitoring and RUL Prediction & Milling & & & & & \\  & & & & & & \\  & & & & & \\  & & & & & & \\ \hline RUL Prediction & Milling & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline RUL Estimation with Varying Spindle Load & & & & & & \\  & & & & & \\  & & & & & \\ \hline RUL Prediction with the Confidence Interval & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: _Cent._ \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Application** & **Machining** & **Algorithm** & **Input Parameters** & **Feature Extraction** & **Feature Selection** & **Results** & **Ref.** \\ \hline Tool State Classification and & & & & & & & & \\ RUL Prediction & & & & & & & & \\ \hline Tool Insert Health & & & & & & & & \\ Monitoring & & & & & & & & \\ \hline Tool Wear Regression & & & & & & & & \\ Estimation in Turning of & & & & & & & & \\ Incerned 718 & & & & & & & \\ \hline Tool Wear Single Prediction & & & & & & & \\ across Multi-Cutting & Turning & & & & & & \\ conditions & & & & & & \\ and SVM & & & & & & \\ \hline Tool Wear Monitoring & & & & & & & \\ Classification & & & & & & \\ \hline Tool Wear Regression & & & & & & \\ Prediction in T-64-IV & Turning & & & & & & \\ Turning & & & & & & \\ \hline Tool Wear Diagnosis & Turning & & & & & & \\ \hline Tool Wear Prediction & Turning & & & & & \\ \hline Tool Wear Monitoring & Turning & & & & & \\ \hline Tool Wear Monitoring and & & & & & & \\ Optimal Process Parameters & & & & & & \\ Selection under input & & & & & & \\ uncertainty & & & & & & \\ \hline Tool Plant Wear & & & & & & \\ Classification & & & & & & \\ \hline Tool Wear Classification & Turning & DT & & & & \\ \hline Rank Wear and Carter Wear & & & & & & \\ Estimation & & & & & & \\ Process Parameters & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._