# Recent Advances on Machine Learning Applications in
## 3 Discussion  
The application of ML methods to machining processes is a challenging task that covers a broad range of domains. The literature presents several recent research, examples and practical experiments, with particular focus on the TCM applications and roughness modeling. The main problems to be solved using ML and DL are often correlated, for example, chatter phenomenon may generate unwanted vibrations that impact on the workpiece surface finishing (roughness) and, consequently, on a quicker degradation of the tool. In the same way, the tool wear is directly correlated both with the final quality of the workpiece and with an increase of the energy consumption. As noted in the different applications, the main strategies are based on the same methods; nevertheless, the integration of new algorithms and schemes are required to satisfy specific conditions. This point may represent a limit to identify a structured rule to implement the ML approach. Table 1 shows an overall summary of the recent literature classified by type of application problem, machining, algorithm, input, features and obtained results. It is noted that milling and turning machining show the most promising results to model and predict the MT behavior. Nevertheless, new achievements have been obtained from other applications that represent the new frontier for ML. In this review, an overall analysis of the recent advances of ML and DL in machining processes has been developed, starting from the application problem. Considering chatter applications, several studies and research with fewer sensors (or even none, if a data-driven model is deployed instead of a real-time recognition system) have been found. Transfer Learning and DL applications are the main implemented methods with satisfying results; despite this, the application of this type of architecture may be more complex and referred for the problem. In this context, significant results are obtained using the SVM or ANN approaches that represent an effective solution. Moreover, in order to have real-time recognition, a sensor should be applied as, for example, vibrations or sound sensors; nevertheless, the limits for industrial implementation should be considered. Roughness prediction models can also be deployed with ML algorithms. As demonstrated, there are two main ways: a regression model and multi-classification system that corresponds to a coarser discretization of the roughness interval considered to reduce the output variability. For these applications, good results were found with Bayesian applications, especially with correct PCA applications for regression problems; for grinding processes and a coarser discretization (multi-classification), good results have been obtained by applying an ANN with a DWT feature extraction method. Finally, DL applications are used for classification problems, where a DBN application with a vibration signal obtained the highest accuracies. Quality problems, together with model problems, are tendentially a case development problem; there are several applications, depending on the needs found by each author in real applications. The literature shows different practical studies with a broad range of algorithms and techniques. The adopted methods depend on the type of problem to be solved. Special interest and promising results were found for thermal deformation modeling and CFRP machining. Machine Condition Monitoring through Deep Transfer Learning is an interesting area, since they allow a greater generalization in their deployment. Most applications were developed with DL structures with few ML algorithms, such as, for example, SVM, but using as the feature extraction a Stacked Autoencoder or Random Forest. Deep Forest showed, together with other DL algorithms, significative results in the backlash error prediction through a DBN. Finally, TCM has been the main source of articles for Smart Machining. As for other applications, two main structures have been found: regression and classification problems, apart from direct and indirect methods.