# Recent Advances on Machine Learning Applications in
## 2 Machine Learning Paradigm in Machining Application
### Machine Condition Monitoring  
Condition monitoring is one of the most recognized application of ML in machining processes. The capacity to predict and detect a failure or identify the wear level of the tool may determine and improve the quality of the production and the performance level of a MT. In particular, the prognostic of the MT component and part behavior is one of the main topics of the state-of-the-art innovations in this area, although the literature shows several studies related to the Tool Condition Monitoring with respect to MT structures condition monitoring, since it may impact on equipment costs and efficiency strongly, as demonstrated in Section 2.6.  
[PERSON] et al. [98] presented a deep transfer learning perspective on condition monitoring based on the CNN approach. Three types of faults were identified: spindle failure, severe tool wear and tool breakdown. Through similarity coefficients, the authors addressed the feature distribution mismatch during transfer learning, on which the CNN was applied based on the maximum mean discrepancy in a Reproducing Kernel Hilbert Space. The experimental results indicated that the approach achieved 94% accuracy in order to evaluate the lifecycle of a MT.  
CNN was also applied for an imbalanced classification problem, as demonstrated in Reference [99], applying the Deep Cost Adaptive CNN configuration to the vibration signal and obtaining a final MAE of 2.5 \(\upmu\)m.  
In order to predict the backlash error in machining centers [100], a Deep Belief Network (DBN) via stacking Restricted Boltzman Machines (RBM) was applied using a number of inputs, such as the number of weeks since the last maintenance, the temperature of the coolant tank, the temperature of the machining center, the ambient temperature, machining torque and the backlash error in the previous week. The result was an MSE of 0.0122 \(\upmu\)m and an ME of 0.2041 \(\upmu\)m. A further extension of the neural-DL structure was the BLSTM-ANN, which considered the FFT and sliding time window of the forces, vibrations and acoustic emissions as the input to predict the Remaining Useful Life (RUL) of a MT [37].  
[PERSON] et al. [101] defined a six-state classification Deep Forest model based on tool wear, chatter and machining deformation. In this case, the FFT and WPT of the vibrations and sound signal were considered as the inputs, while the Lasso technique was implemented to select the model features.  
An interesting application of MT performance monitoring and fault classification was developed with a Random Forest structure in Reference [21] with the FFT, peaks and RMS of the vibration signal from the spindle. The obtained results showed a structured approach to monitor the machine health and performance in real time.  
The last tree-like structure was applied in [PERSON] et al. [102] for the failure prediction in cyber-physical productions system using the spindle speed, spindle power and vibration level. The authors extracted the features through the Dynamic Principal Component Analysis, and a Gradient Boosting Decision Tree was applied as the model. The experimental results indicated that the accuracy of the predicted production failures using the proposed predictive tool was close to 73%.  
Finally, [PERSON] et al. [103] presented an interesting study to classify the bearing fault diagnosis using the FFT of vibration data as an input signal, from which a Stacked Autoencoder extracted the features applied by a Least-Squares Support Vector Machine and optimized by the Chemical Reaction Optimization Algorithm.