# Recent Advances on Machine Learning Applications in
## 1 Introduction
DL applications are used as feature extraction and selection methods (4 to 5) for ML applications, proving to be a reliable and efficient application based on the obtained results, as shown in References [29; 30]. Moreover, it allows the implementation of simpler ML methods, such as k-nearest neighbors (k-NN) or simple ANNs or a Decision Tree (DT). Beside this application, the actual use of DL algorithms in machining in the substitution of ML reduces the number of the required steps, since feature extraction and selection (4 to 5) is typically done by the same model [31; 32]. These steps may improve the final results of the DL applications, as highlighted in References [33; 34]. The main limits of DL algorithms incur when the acquired data is not enough to train the models correctly, for example, breakages or malfunctioning data tend to be reduced compared to the massive amount of data collected in conventional operational conditions of a machine tool, which may lead the model to biased situations. A further extension of ML applications is transfer learning (TL), as shown in References [35; 36], that consists of storing information and knowledge obtained in solving one problem and applying them to a different but related problem. This approach may be used in order to: (1) reduce the learning time required, (2) avoid the first problematic steps in learning for ML and DL and (3) improve the results. In recent years, the application of these AI methods has rapidly expanded in machining processes. Their applications are typically based on nonlinear time variant problems that can be clustered into five groups:  
* Condition Monitoring [37; 38]: it consists of monitoring process parameters (e.g., temperature, vibrations, accelerations and tool wear) in order to predict the MT conditions, for example, it allows the correct definition of the tool replacement time and reduces the periods of time that the machine is stopped due to critical breakages. In this case, further approaches may be used, such as Tool Condition Monitoring (TCM) and Condition Monitoring (CM--related to the current machining structure). * Chatter [39; 40]: it is a self-excited vibration caused by the continuous interaction between the tool and the workpiece that creates several issues in machining: the ability to correctly determine or predict when chatter occurs reduces the probability to refinish the workpiece and the corresponding tool wear. * Quality [41; 42]: the thermomechanical behavior of the machining structure may generate unwanted tool tip displacements, which would increase the final machining error. In this case, the main issue is related to the quality level of the surface roughness, which is one of the main requirements in machining. * Modeling [43; 44]: there are several applications that require to model and predict a phenomenon related to the machine technology, for example, it is useful to determine the correct material removal or the most suitable cooling/lubricating technology to improve the equipment sustainability. Energy [45; 46]: the energy consumption prediction of a machine tool is becoming increasingly critical in terms of emission reduction and energy efficiency of the manufacturing processes. The application of ML techniques permits to predict the most suitable MT setting to save energy in machining, guaranteeing the required production performance. Although the literature presents various ML applications in machining [47; 48; 49; 50], further studies are required to investigate the recent advances in algorithms and approaches in development. In particular, the increase of the ML extension shows the need of a set of structured rules to allow an effective comparison between different applications and results.