###### Abstract  
The drilling of carbon fiber-reinforced plastic (CFRP) materials is a key process in the aerospace industry, where ensuring high product quality is a critical issue. Low-quality of final products may be caused by the occurrence of drilling-induced defects such as delamination, which can be highly affected by the tool conditions. The abrasive carbon fibers generally produce very fast tool wear with negative effects on the hole quality. This suggests the need to develop a method able to accurately monitor the tool wear development during the drilling process in order to set up optimal tool management strategies. Nowadays, different types of sensors can be employed to acquire relevant signals associated with process variables which are useful to monitor tool wear during drilling. Moreover, the increasing computational capacity of modern computers allows the successful development of procedures based on Artificial Intelligence (AI) techniques for signal processing and decision making aimed at online tool condition monitoring. In this work, an advanced tool condition monitoring method based on the employment of autoencoders and gated recurrent unit (GRU) recurrent neural networks (RNN) is developed and implemented to estimate tool wear in the drilling of CFRP/CFRP stacks. This method exploits the automatic feature extraction capability of autoencoders to obtain relevant features from the sensor signals acquired by a multiple sensor system during the drilling process and the memory abilities of GRU to estimate tool wear based on the extracted sensor signal features. The results obtained with the proposed method are compared with other neural network approaches, such as traditional feedforward neural networks, and considerations are made on the influence that memory-based hyperparameters have on tool wear estimation performance. composite materials; drilling; tool wear; sensor monitoring; deep learning; autoencoder; gated recurrent unit +
Footnote †: journal: Medical  
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.