# Recent Advances on Machine Learning Applications in
## 2 Machine Learning Paradigm in Machining Application
### Modeling  
Machine learning and Deep Learning techniques may be also exploited to model the behavior of a number of MT components and structural parts. Interesting applications are related to the prediction of the process forces on the workpiece and the computation of coefficients to define the stability of the machining. These applications could be seen as a \"virtual sensor\" generator. [PERSON] et al. [84] developed an ANN scheme to model the cutting force in end milling operations. The authors studied the process parameters and the rotation angle to generate the necessary data to train the ML model, obtaining a RSME of 1.0058 and an R\({}^{2}\) of 99.98%. This structure was also implemented to develop a Reinforcement Learning (RL) structure based on an ANN [85]. The aim was to limit the resulting force in press-and-release systems, obtaining a resulting final roughness reduction of 30.68%. A further example of RL application was described in Reference [86] to predict the optimal clamping position of the workpiece. Neural Network applications have been applied for tool tip dynamics, stability and optimization problems [36; 44; 87; 88]. [PERSON] et al. [89] considered Neural Networks, under the form of CNN, based on camera images of the metal cutting processing for machining parameters extraction, obtaining a model accuracy of 85.5% and a precision of 92.9%. In the same way, a Convolutional Neural Network structure was implemented with ResNet configuration, considering the responsive fixtures and process data as input, in order to allow the Bidirectional LSTM model to predict the part deformation with an error equal to 10.61% [29]. LSTM was also applied to model the dependency between the deviation, tensile force and eccentricity of low-rigidity shaft machining with a MSE of 1.5456 \(\times\) 10\({}^{-5}\)[90]. In order to predict the dynamic heat input to monitor the robotic belt grinding [91], [PERSON] et al. presented a Bayesian Adaptive Direct Research-Least Squares Support Vector Machine (BADS-LSSVM) with a RBF kernel, considering the sound signal and the process forces (e.g., normal and tangential). In this case, the force features were obtained using the moving smoothing filter method and extracting the Dynamic Friction Coefficient. The Wavelet Package Decomposition method was applied for the sound signal, calculating several features such as average amplitude, kurtosis and zero passage rate. SVM may be also applied to model and control the trajectory [92] and several MT parameters, such as the tool velocity, the roughness generation (90% of accuracy) and the part features (e.g., diameter) prediction (97% of accuracy), as demonstrated in Reference [93]. The models are accurate enough to provide useful conclusions applicable to the current industrial practices. [PERSON] et al. [94] applied an Extreme Learning Machine (ELM) in milling for the time estimation of cycloidal gear machining based on the design and manufacturing parameters of the gear. The study obtained a RMSE of 1.6837 and an R\({}^{2}\) of 99.34%. A further example in milling was presented by [PERSON] et al. [95], who evaluated the machining mode, state and motor temperature (regression) predictions by applying the ML structure. The authors implemented the process parameters (e.g., axis position, cutting tool position and machine speed) as the model input. [PERSON] et al. [43] considered a tree-based structure in modeling the material removal, where the DWT and FFT of the acoustic signal was used as input for the k-fold XGBoost model, obtaining a MAPE of 4.373%. Finally, interesting applications of ML focus on Energy modeling in order to optimize the MT consumption, guaranteeing the machining performance level. [PERSON] et al. [45] applied Hidden Markov Modeling using the power signal as an input while using dynamic time warping with hierarchical clustering for the feature extraction. The authors obtained a MAPE of 1.12%. Considering the instantaneous power and process parameters, [PERSON] et al.