Tool condition monitoring have an important role in machining processes to reduce defective component and ensure quality requirements. Stopping the process before the tool breaks or an excessive tool wear is reached can avoid costs resulting from that undesirable situation. This research work presents the results obtained in drilling process monitoring carried out on Inconel 718. Monitoring systems should be light and scalable. Following this idea, multiple sensors for external signal acquisition are used in this work (cutting forces, vibrations, and acoustic emissions) and several machine internal signals are collected. The main objective is to evaluate the capacity of each acquisition source for the reconstruction of the tool wear curve and subsequently detection of tool breakage. Given the difficulty of using all of these signals in a real system, the methodology used to analyse the data makes it possible to have a comparative analysis of the potential of each of these sources for tool wear monitoring during the drilling process. The results indicate cutting forces whether they come from internal signals or external signals can carry out this task accurately. At the same time of data acquisition, detailed tool wear measurements were added.

PEO constitutes a promising surface technology for the development of protective and functional ceramic coatings on lightweight alloys. Despite its interesting advantages, including enhanced wear and corrosion resistances and eco-friendliness, the industrial implementation of PEO technology is limited by its relatively high energy consumption. This study explores the development and optimization of novel PEO processes by means of machine learning (ML) to improve the coating thickness. For this purpose, ML models random forest and XGBoost were employed to predict the thickness of the developed PEO coatings based on the key process variables (frequency, current density, and electrolyte composition). The predictive performance was significantly improved by including the composition of the used electrolyte in the models. Furthermore, Shapley values identified the pulse frequency and the TiO₂ concentration in the electrolyte as the most influential variables, with higher values leading to increased coating thickness. The residual analysis revealed a certain heteroscedasticity, which suggests the need for additional samples with high thickness to improve the accuracy of the model. This study reveals the potential of artificial intelligence (AI)-driven optimization in PEO processes, which could pave the way for more efficient and cost-effective industrial applications. The findings achieved further emphasize the significance of integrating interactions between variables, such as frequency and TiO₂ concentration, into the design of processing operations.

Lithium-ion batteries have emerged as a prevalent power source in a variety of industries, including the electric vehicle sector due to their higher energy density and low self-discharge. With the use and passage of time, batteries degrade and eventually die, endangering the integrity of the objects they power. The ability to accurately predict the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for optimizing their utilization and ensuring their safe operation. For this purpose, a deep learning-based approach trained on the widely used Oxford battery degradation dataset with the help of Generative Adversarial Networks (GANs) has been implemented. The designed network consists of a Long-Short-Term Memory (LSTM) architecture with the implementation of a stratification strategy and a custom loss function. The illustrative results show that the suggested approach can produce adaptable and reliable predictions of the RUL.

The manufacturing industry is constantly seeking innovative solutions to optimize machining processes. However, there is a lack of efficient digital platforms that fully meet the flexibility, service composition, and affordability needs of the manufacturing industry, in particular for small and mediumsized enterprises (SMEs). This paper introduces the OptiTwin platform, a novel data-driven system designed to enhance machining process optimization for SMEs. The OptiTwin platform was developed with a focus on data acquisition, management, and analysis based on data driven models. The functionalities of the platform were validated through a drilling use case at Mondragon University's high-performance machining laboratory, demonstrating its effectiveness in real-time tool condition monitoring. The results showcase the potential of OptiTwin in optimizing machining processes and empowering SMEs with data-driven insights for enhanced productivity and quality assurance.

Within the framework of industry 4.0, the aim is to develop collaborative human-machine environments in order to achieve greater adaptability to the variability of cutting processes by making efficient use of available resources. For this, the use of the existing information in the data obtained from the cutting processes is fundamental. The control and visualization of scientific parameters (acoustic emissions, cutting powers, vibrations, shear forces...) related to industrial parameters (tool wear, roughness, microstructure...) in drilling processes is of great importance. Drilling processes are carried out in the final stages of the production of a part, which often results in a critical operation. Using an experimental setup, where both internal signals and the acoustic emissions signal are acquired and through the use of automatic learning algorithms, a qualitative estimate of the quality of the hole made is performed. Given the demands of sectors in which it is necessary to check the roughness of the machined surface and taking into account the requirements to be met by manufacturing companies, obtaining an indicator of the state of the machined surface is an advantage in terms of decision-making. Keywords: Roughness, acoustic-emission, machine-learning, drilling