# Recent Advances on Machine Learning Applications in
## 1 Introduction
In fact, the application of ML algorithms increases the machining capabilities, obtaining more robust conditions in monitoring systems and models, since they are trained from data through seven main steps: (1) problem definition, (2) data collection, (3) signal processing, (4) feature extraction, (5) feature selection, (6) model implementation and (7) model validation. The first step is the problem statement (1). The correct selection of input data determines the capability of the deployed model. Typical choices in machining are the measurements of the process forces [12; 13], accelerations [14; 15], vibrations or acoustic emission sensors [16; 17] (2-3). Furthermore, the availability of the inner sensors or process parameters allow simple and reliable applications [18; 19], including all the data available during the production. The use of new external sensors may be evaluated in the details, since it may increase the time and cost of the study and not imply better results in terms of accuracy for the ML application. In order to use data collection from the different sensors, a signal treatment (4) is required for correct feature extraction. Typically, there are three main domains: (i) Time Domain (TD), (ii) Frequency Domain (FD) and (iii) Time-Frequency Domain (TFD). For each domain, there are typical coefficients, for example, statistical features (e.g., kurtosis, standard deviation) are used for TD signals [20], while other types of features are obtained (e.g., RMS and peak values [21]) for FD signals. This analysis permits to understand those features that should be used in the prediction of the ML model, excluding those signals that may create noise in the system when incrementing the ML complexity (5). This step can be developed using wrapper methods, which consist of testing the result variations for different settings of the input data. In order to improve the selection of features, further methods, called filters, may be considered that classify the features based on external criteria. In this case, PCA (Principal Component Analysis) [22; 23] and PCC (Pearson Correlation Coefficient) evaluations may be applied [24; 25]. The filters could be used as the first processing phase, while the wrapper methods could be implemented to obtain the optimal feature selection for the ML applications [26; 27]. Once these steps are defined (the feature selection with wrapper methods is completed only after the ML model selection), the ML model can be applied (6). The selection of the correct ML algorithm depends on the type of problem (e.g., regression or classification approach). The model selection is influenced by the dataset preprocessing activity for a robust comparison between the prediction methods to maintain the generalization ability of the architecture. Cross-validation techniques are commonly applied to determine the best model. The conventional method separates the data into training and testing substructures. The groups require that the training dataset contains representative conditions. Several different approaches have been developed, such as k-fold cross-validation or leave-p-out cross-validation [28], to allow a superior model design. The limitation is represented by the time consumption and computational requirements. However, the ability to predict data out-of-learning edges is a central characteristic of Machine Learning applications. In this way, the wide availability of ML algorithms suggests comparing different methods in order to identify the most suitable scheme (7), with limited from time-consuming work to collect, process and analyse data. In particular, an increment of sensors may be required, resulting in big volumes of data. These available datasets allow further extensions, such as Deep Learning (DL) applications that, differently from ML, do not obtain a limited learning rate due to the amount of data.