# Recent Advances on Machine Learning Applications in
## 2 Machine Learning Paradigm in Machining Application
### Chatter  
Chatter is a self-excited vibration that occurs in machining (e.g., milling and turning) while operating at a high speed. This is an undesirable phenomenon that has negative effects, such as a poor surface finish, unacceptable accuracy, excessive noise and tool wear. A conventional structured approach is the application of the stability lobe diagram (SLD) that determines the process parameters to anticipate unwanted vibrations. SLD is used to label the data; nevertheless, the system is learnt from analytical models, and therefore, it requires effective initial modeling. In this way, ML may offer new opportunities for chatter online recognition, employing extra sensors (e.g., accelerometers). [PERSON] et al. [51] used the process parameters to train and compare a number of models: an ANN, a support vector machine (SVM) and a new approach based on kernel interpolation (KI) to predict the SLD. The study was applied to a five-axis milling center with a milling tool of 10 mm in diameter and four teeth. The results showed that the ML-based creation of SLD may be a good alternative to analytically calculated SLD. All the models achieved accuracies over 88%. In particular, the KI model obtained the highest accuracy (94%). The chatter phenomenon was collected using acceleration sensors and a microphone. The main limit of this application was the availability of data when the system changed the conditions. For this reason, [PERSON] et al. [19] proposed a new approach based on Deep Neural Networks and transfer learning, since DNN models are pretrained with simulated data from analytical stability models. The aim was to reduce the differences between the real measurements and the model output due to the cutting forces and the tooltip dynamics. The study obtained an accuracy of 83.6% for the testing set, using as inputs the spindle speed, depth of cut and tool clamping length, as well as the entry angle and exit angle. Another transfer learning example with the Random Forest (RF) algorithm was studied in Reference [35], where the vibration signal was used together with the stick out length, the Ensemble Empirical Mode Decomposition (EEMD) was applied as the feature extraction and the Recursive Feature Elimination (RFE) method was executed to classify the machining state into three categories: no chatter, mild chatter and chatter. The final results showed that, for transfer learning feature extraction, the EEMD outperformed Wavelet Package Transform (WPT). [PERSON] et al. [52] focused on the feature extraction method to determine the presence of chatter based on the simulated oscillations of the milling tool (1 DOF) data with high noise. They implemented the Topological Data Analysis--more specifically, Carlsson Coordinates and Template Functions--obtaining high accuracies with several algorithms; the ones with the highest accuracies were the SVM and Gradient Boosting. Binary chatter classification employing vibration signals in the input also included SVM with frequency domain features in Reference [53] and ANN in Reference [54], where the statistical time domain features were used together with a DT (J48) for feature selection. Other than accelerometers, sound sensors are sometimes considered for chatter recognition, as in Reference [55], where the binary classification was obtained with a SVM, the short-time Fourier-transform (STFT) features and an Autoencoder for dimension reduction. Mixing sound signals and vibration information has also been implemented in Reference [56] with the t-distributed stochastic neighbour embedded to obtain the final features and the Reinforced k-NN for chatter prediction. Finally, force signals were employed for chatter classification into three classes, in Reference [13] with the Gradient Tree Boosting algorithm, the ASA for feature extraction and the Laplacian score for the final selection; in Reference [12], a convolutional neural network (CNN) was implemented based on the features extracted from the continuous wavelet transform (CWT).