###### Abstract
As a matter of fact, the sensor signals need to be described by a reasonable number of SFs that keep the relevant information on the monitored machining process with the aim of effectively supporting decision making. Different signal processing methodologies, which generally include a data pre-processing step followed by feature extraction, selection, classification, and validation, can be employed. Decision making on the basis of the extracted SFs can be performed using different methodologies, e.g., based on machine learning and deep learning approaches. In this case, it is particularly important to automatically generate SFs that are as independent as possible from each other and then select the most effective ones to reduce the problem dimensionality and hence the computational effort and time. Nowadays, the increasing computational capacity of computers allows the development of different approaches based on Artificial Intelligence (AI) methods [8]. These approaches are ever more used for the development of industrial applications in several fields, such as welding [9], additive manufacturing [10], and in-line defect detection [11]. In the literature, different solutions have been proposed to deal with tool condition monitoring using these techniques. [PERSON] al. [12] compared the results in tool wear estimation of the regression tree, support vector machine (SVM), Gaussian process regression (GPR), and artificial neural network (ANN) algorithms using as inputs the cutting speed and feed rate process parameters. [PERSON] et al. [13] used thirteen statistical features extracted from the raw audio signals, namely mean, standard error, median, mode, standard deviation, sample variance, kurtosis, skewness, range, minimum, maximum, sum, and count, and used them as input to a binary K-star classifier, aiming to identify if a tool is still able to work or needed to be changed. [PERSON] et al. [14] compared the performance of different ANN architectures in the estimation of CFRP drilling tool wear for different process parameters. Statistical features, namely mean, standard deviation, energy, kurtosis, and skewness, are extracted from the force, torque, acoustic emission, and vibration signals to construct a Sensor Fusion Pattern Vector (SFPV) composed of the principal components obtained via principal component analysis (PCA) of 8 statistical features, selected from the original 20 using the Pearson correlation coefficient, and the number of holes made by the tool. The SFPV vector constructed in this way was used as input to ANN architectures. [PERSON] et al. [15] used the process parameters and the RMS current absorbed from the spindle motor acquired during the drilling of different materialsto train different ANN architectures and compare results with a simple regression model. The results show that the current absorbed from the spindle motor has a high correlation with tool wear since it is associated with an increment of the drilling force. [PERSON] et al. [16] used the statistical features, namely max value, median, mean, and standard deviation of cutting force signals and vibration along different directions (x, y, and z) and the same value for the acoustic emission signal to train and compared results in tool wear estimation of different algorithms such as Random Forest, Support Vector Machine and different ANN architectures. Thanks to the huge improvements achieved in deep learning approaches, especially due to the capabilities of automatic feature representation, also Convolutional Neural Networks [17], Recurrent Neural Networks [18], and autoencoder architectures [19] have attracted considerable attention for tool wear prediction research. In particular, [PERSON] et al. [18] proposed a prediction model for tool flank wear using an LSTM model network, using the spindle motor current signals during a machining process as input feature. [PERSON] et al. [19] employed an autoencoder architecture to automatically extract features correlated with the failure of a cutting tool using an unsupervised learning approach.