# Recent Advances on Machine Learning Applications in
## 2 Machine Learning Paradigm in Machining Application
### Quality  
The literature shows a number of ML applications to improve the overall performance level and quality of a machine tool (MT). In this way, [PERSON] et al. [41] presented an ANN application (Multi-Layer Perceptron--MLP) to model the MT thermomechanical deformations of CFRP (Carbon Fiber Reinforced Polymers) structures based on the global variations of the temperature, the gradient of the temperature between the front and rear of the ram, the gradient between the spindle flange and the vertical axis and the environmental condition temperatures. Authors have reduced the detected error under 10 \(\upmu\)m. [PERSON] et al. [68] used DL applications for thermal deformation modeling using data mining based on RST and reducing the thermal error of \(\cdot\)99%. [PERSON] et al. [69] focused on thermal displacement prediction, applying a DNN with Bayesian Dropout and considering the sensor failures to test the robustness of the model. In Reference [70], the research aimed to predict the thermal drift based on four different working conditions; the authors applied pretrained coefficients for the CNN initialization, obtaining a so-called CNN-FT (fine-tuning). A model accuracy of 87.06% with an MSE of 0.0124 and a MAPE of 0.2154 was obtained. [PERSON] et al. [71] applied the CNN approach and a Domain Adaptation Module for the thermal error prediction, achieving a model accuracy over 94.87% and an MSE under 6.1 \(\times\) 10\({}^{-6}\).  
The CNN scheme is also applied to detect workpiece surface defects [72], using, as the input, the scattering data from a laser beam and simulated data for training to reduce the required time. A further application to detect edge inconsistencies from images was demonstrated in Reference [73]. The quality error evaluation on the workpiece was evaluated in several researches by analyses performed with tree-based algorithms. For example, [PERSON] et al. [74] applied RF Ensemble combined with the Synthetic Minority Over-Sampling Technique (SMOTE) for flatness deviation prediction based on the tool's life, average drive power and flank wear, with a testing accuracy of 86.44%. RF was also implemented for the diameter, roundness and other quality parameters using torque values and speed statistical time-domain features [75] or the axial force and torque together with the process parameters [76]. Finally, an Extreme Tree Regressor (ETR) was considered for the workpiece diameter and concentricity control in drilling and reaming [77].  
A further ML approach is the Support Vector Machine. [PERSON] et al. [78] showed a study on the residual properties of the ball screw raceway in dry machining using a Gaussian RBF kernel and the combination of SVM with the Least Mean Square method (LSSVM). The authors considered as input the cutting parameters, tool parameters and the machining condition (clamping coefficient). RBF kernels were also applied for tool deflection modeling [79], obtaining a 94% accuracy, including the SVM (k-RBF) based on LIBSVM. [PERSON] et al. [80] predicted the martensite content after cryogenic turning, considering the passive, cutting and feed forces and temperature, applying the PCC approach for feature selection and obtaining a RMSE of \(\sim\)0.8. Linear kernel was also applied for flatness classification prediction in honeycomb cores using the time-domain and frequency-domain (based on FFT) features of the force signal and the PCA for feature reduction [81; 82]. Finally, [PERSON] et al. [83] implemented a Gaussian Process with Polynomial Kernel to predict the SR peculiarities of WEDM.