# Recent Advances on Machine Learning Applications in
## 2 Machine Learning Paradigm in Machining Application
### Roughness  
Roughness estimation and prediction may be evaluated through a set of models based on two main clusters: classification and regression. The classification models permit a discretization of the interval of the regression values, as demonstrated in [PERSON] et al. [57], where the roughness, from 0 to 300 nm, was discretized in 150 clusters of 2 nm each. Despite limiting the maximum actual accuracy in the final roughness value, the discretization allowed to increment the classification accuracy and reduce the output variability. The vibration signals measured with a laser vibrometer were applied in the t-Distributed stochastic neighbour embedding (t-SNE) method for feature selection to enhance the capability of the CNN, obtaining high accuracies in their predictions. The classification approach may generate a similar regression prediction with a high amount of classes; nevertheless, other applications [58; 59; 60] defined few levels (three to four) of surface roughness for the prediction, guaranteeing effective results. In this way, [PERSON] et al. [58] used the vibration signal and process parameters with a DL method--namely, the Knowledge-Based Deep Belief Network (KBDBN)--to classify the roughness value within three levels, obtaining a recognition rate of 97.67%. Differently, other studies based on tree algorithms used the model to classify the roughness within four levels. In this case, [PERSON] et al. [59] implemented the Deep Forest, a DL method based on the WPT and Fast Fourier-Transform (FFT) features of forces and load signals to predict the roughness class, obtaining a testing accuracy of 90.91%. Finally, [PERSON] et al. [60] applied a semi-supervised structure based on Random Forest; although the accuracy was low if compared to the other results (around 70%), the authors showed the potential contribution of unlabeled data in improving the model accuracy. Considering the regression predictions, the renowned algorithm is the ANN: [PERSON] et al. [61] used double-modeling (BP-ANN) to predict the cutting force and the final roughness. [PERSON] et al. [62] applied ANOVA as a feature selection method to predict roughness in wire electrical discharge machining (WEDM). [PERSON] et al. [63] proposed a roughness prediction model based on the discrete wavelet transform (DWT) and RMS peak value of the acoustic emission, and the grinding parameters obtaining accuracies over 97%. Finally, [PERSON] et al. [42] studied the acoustic emission, forces and current measurements through PCA feature reduction to obtain through the ANN the correct time for tool changesbased on the roughness prediction, obtaining a mean absolute percentage error (MAPE) of 4.2% in their prediction. [PERSON] et al. [64] also implemented an ANN-based model (Adaptive Neuro-Fuzzy Inference System--ANFIS) based on the Takagi-Sugeno Fuzzy Rules and the Quantum Particle Swarm Optimization Algorithm (QPSO) to predict and optimize the surface roughness in turning. The only DL application was also based on a neuro-like model; the authors showed how 1D-CNN is able to extract high feature information in the medium range of surface roughness, whilst the FFT-LSTM (long short-term memory) structure obtained better results at higher roughness values, where its temporal modeling is advantageous [65]. [PERSON] et al. [66] considered Gaussian modeling and Bayesian learning to implement a surface roughness estimation in ring-shaped thin-walled discs to reduce the number of measurements required based on measured values along different trajectories. Another Bayesian application [22] applied as an input the vibration and process parameter time domain features and the radial basis function-based kernel principal component analysis (KPCA-IRBF) as a feature selection to apply the Sparse Bayesian Linear Regression (SBLR) for the roughness prediction, obtaining a Root Mean Square Error (RMSE) of 0.0317 and a Pearson Correlation Coefficient of 0.9926. Finally, [PERSON] et al.