The use of new structural materials such as carbon fiber reinforced plastics (CFRP) allows for substantial weight reduction on aircraft, which positively impacts CO
2 emissions as well as management costs due to lower fuel consumption, consistent with today’s requirements for environmental sustainability [
1]. For the assembly of aeronautical CFRP components, mechanical joining techniques such as riveting are widely employed to achieve strong and reliable joints. Consequently, the most widespread CFRP machining process in the aerospace industry is represented by drilling, which is needed to realize the holes for subsequent riveting. However, the anisotropic nature of composite materials, the very rapid tool wear growth due to the abrasive carbon fibers, and the intense stresses and vibrations, which can cause damage to material integrity, surface quality, and part aspects, make drilling of CFRP components a major manufacturing challenge [
2].
In the aeronautical industry, where severe requirements are applied to geometrical and dimensional tolerances as well as surface integrity, the current practice for CFRP drilling consists of manual, semi-automated, or automated drilling processes where tools are replaced long before the end of tool life to avoid any risk of material damage. As a matter of fact, tool wear estimation is a complex task to solve because several wear mechanisms occur simultaneously during machining [
3], and direct measurement is generally unfeasible in an industrial context. Hence, the problem of tool wear is often managed in the industry by using lower cutting parameter values with the aim of slowing the wear process and replacing the tool well before the expected end of life [
4]. To overcome these inefficient procedures, different methodologies, including empirical [
5] and stochastic modeling techniques [
6], have been proposed in the literature, suggesting new strategies for tool management. One of the most effective approaches to fully exploit tool life and increase productivity while preserving the integrity of the work material is the implementation of a procedure able to accurately monitor the tool wear development online in order to set up optimal tool management strategies based on the actual tool conditions. Online real-time process monitoring based on the employment of sensor systems and advanced sensor signal processing procedures is a valuable solution for tool condition monitoring [
7], allowing for in-process control of tool wear growth which is critical for hole quality assessment and drilling process effective automation. Multiple sensors of different natures can be employed to acquire various signals associated with the most relevant process variables, e.g., force, torque, vibrations, etc., which can be useful to monitor tool wear during machining. A fundamental matter in machining process monitoring is the identification of relevant sensor signal features (SFs) from the acquired sensor signals, which are well correlated with process conditions. 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. Hegabet 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. Simon 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. Caggiano 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. Patra et al. [
15] used the process parameters and the RMS current absorbed from the spindle motor acquired during the drilling of different materials to 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. Wu 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, Marani 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. Sun et al. [
19] employed an autoencoder architecture to automatically extract features correlated with the failure of a cutting tool using an unsupervised learning approach. Shah et al. [
20] compared the results for tool wear estimation using vibration and audio signals with LSTM and bidirectional LSTM. In particular, the input vector for the networks was composed of features extracted by a scalogram generated by a Morlet transform and a generative network, as a data augmentation method.
The literature review about the state-of-the-art methods for tool condition monitoring, with particular reference to the machining of composite materials, shows that the employment of automatic feature extraction techniques, which allow the complete automation of the tool wear estimation process, has not been fully explored. As regards the employment of recurrent neural networks, some applications have been reported in the literature to estimate tool wear in the machining of metals, but very few applications have been proposed in the machining of composite materials. Moreover, most of the proposed applications generally employ manual feature extraction methods, which, on the one hand, require a strong knowledge of the problem by the developer to extract useful information, and, on the other hand, imply some simplification hypotheses, such as considering the data Gaussian distributed or linearly related. To tackle this issue, the specific contribution of the present work is related to the development of a new integrated method for tool wear prediction in drilling based on the employment of autoencoders for automatic feature extraction and gated recurrent unit (GRU) memory-based recurrent neural networks for tool wear prediction. This method can exploit the potential of both models for tool wear prediction and represents an advancement compared to the literature, allowing the full automation of both feature extraction and tool wear prediction tasks. The proposed method is developed in this work to realize tool condition monitoring in the drilling of CFRP/CFRP stacks for aeronautical assembly based on force, torque, vibration, and acoustic emission sensor signals.
Different models are developed and assessed, also by comparing the results obtained with the proposed method with those achieved using other neural network approaches, such as traditional feedforward neural networks, and considerations are made of the influence that memory-based hyperparameters have on tool wear estimation performance.