This repository is maintained by the Digitalisation Group of the CFAA, a research centre of the University of the Basque Country. Currently, the research group is formed by:

• Unai López-Novoa, Associate Professor of Computer Science.
• Leonardo Sastoque-Pinilla, doctoral researcher and coordinator of IT and R&D projects.
• Endika Tapia-Fernández, researcher on big-data and data systems management topics.
• Nahia Iturregi Artiñano, researcher on knowledge management and advanced retrieval techniques.

• High performance computing
• Scalable data processing systems
• Industrial communication protocols
• Cloud and edge computing
• Heterogeneous and emerging architectures
• Energy efficient computing

2025

• Tapia, E., Sastoque-Pinilla, L., Gamecho, B., & Lopez-Novoa, U. (2025). Integration of blockchain-based digital identity management in an aeronautical manufacturing setting. The International Journal of Advanced Manufacturing Technology, 1-15.
• Sáinz de la Maza García, Á., Sastoque-Pinilla, L., & Villarrazo-Rubia, N. (2025). Measuring thermally induced volumetric error of large-scale machining centre. The International Journal of Advanced Manufacturing Technology, 1-14.
• Tapia, E., Lopez-Novoa, U., Sastoque-Pinilla, L., & López-de-Lacalle, L. N. (2025). An Evaluation of Large Language Models for Code Optimization. In 2025 10th International Conference on Smart and Sustainable Technologies (SpliTech) (pp. 1-5). IEEE.
• Sáinz de la Maza García, Á., Sastoque Pinilla, L., & López de Lacalle Marcaide, L. N. (2025). Influence of Structural Components on Thermal Deformations in Large Machine Tools. Journal of Manufacturing and Materials Processing, 9(8), 267.

2024

• Tapia, E., Lopez-Novoa, U., Sastoque-Pinilla, L., & López-de-Lacalle, L. N. (2024). Implementation of a scalable platform for real-time monitoring of machine tools. Computers in Industry, 155, 104065.
• Sendino, S., Sastoque-Pinilla, L., del Olmo, A., & de Lacalle, L. N. L. (2024). Tool Fracture Detection in Electromechanical Broaching Through Machine Sensor. Procedia CIRP, 122, 994-999.
• Nieto, G., De la Iglesia, I., Lopez-Novoa, U., & Perfecto, C. (2024). Deep Reinforcement Learning techniques for dynamic task offloading in the 5G edge-cloud continuum. Journal of Cloud Computing, 13(1), 94.

2023

• Aldekoa, I., del Olmo, A., Sastoque-Pinilla, L., Sendino-Mouliet, S., Lopez-Novoa, U., & de Lacalle, L. N. L. (2023). Early detection of tool wear in electromechanical broaching machines by monitoring main stroke servomotors. Mechanical Systems and Signal Processing, 204, 110773.
• Tapia, E., Sastoque-Pinilla, L., Lopez-Novoa, U., Bediaga, I., & López de Lacalle, N. (2023). Assessing industrial communication protocols to bridge the gap between machine tools and software monitoring. Sensors, 23(12), 5694.
• Sen, S., Nielsen, S. M., Husom, E. J., Goknil, A., Tverdal, S., & Pinilla, L. S. (2023, May). Replay-driven continual learning for the industrial internet of things. In 2023 IEEE/ACM 2nd International Conference on AI Engineering–Software Engineering for AI (CAIN) (pp. 43-55). IEEE.

2022

• Tapia, E., Sastoque-Pinilla, L., De Lacalle, N. L., & Lopez-Novoa, U. (2022, July). Towards real time monitoring of an aeronautical machining process using scalable technologies. In 2022 7th International Conference on Smart and Sustainable Technologies (SpliTech) (pp. 1-6). IEEE.
• Del Olmo, A., De Lacalle, L. L., De Pissón, G. M., Pérez-Salinas, C., Ealo, J. A., Sastoque, L., & Fernandes, M. H. (2022). Tool wear monitoring of high-speed broaching process with carbide tools to reduce production errors. Mechanical Systems and Signal Processing, 172, 109003.

2021

• Del Olmo, A., de Pissón, G. M., Sastoque, L., Fernández, A., Calleja, A., & De Lacalle, L. L. (2021, October). Merging complex information in high speed broaching operations in order to obtain a robust machining process. In IOP Conference Series: Materials Science and Engineering (Vol. 1193, No. 1, p. 012079). IOP Publishing.

• Machine consumption models for energy efficiency.
• Virtual sensors for machine monitoring.
• Deployment of an architecture for the homogenization of manufacturing data.
• Embedded systems for monitoring cooling systems for machining processes.
• Machine learning for automatic tool wear monitoring.
• Big data platform for distributed processing systems (EDM, Broaching, etc.)
• Study of industrial communication protocols (Profinet, Profibus, MQTT, etc.)
• Big data for technology monitoring.
• Sustainability and Environmental Impact Study of the CFAA using an Intelligent Sensor-based Approach.