Collaborative Projects

Predigitenibleto is an innovative project aimed at revolutionizing sustainability and energy efficiency in industry through the application of advanced Industry 4.0 technologies. Its core objective is the development of an intelligent monitoring and control system for refrigeration and compressed air installations, combining artificial intelligence, machine learning, and digital twin technologies to optimize energy performance.

The main objective of this project is the recovery and revalorization of plastic waste generated during the manufacturing of vehicle interior components. It seeks to reduce energy and raw material consumption, as well as the environmental impact associated with products and processes. This initiative is designed to support manufacturers in transitioning toward more sustainable mobility, integrating circular economy solutions into the automotive value chain.

This project focuses on the optimization and recovery of composite materials through the implementation of advanced and robotic technologies, accelerating the transition toward climate neutrality, improving energy efficiency, and fostering sustainable resource use. A key outcome will be the development of a collaborative robot specialized in the recovery and reuse of composite materials, enabling industry to make a decisive step toward the circular economy.

Now in its third phase, this project aims to advance research into flexible additive manufacturing technologies, incorporating advanced robotics, intelligent systems, computer vision, artificial intelligence, and digital twin applications for sustainable materials. The integration of these technologies will enhance 3D printing processes, achieving greater efficiency, sustainability, and zero-defect manufacturing, with a clearly positive environmental impact.

The SOcHarger-IA project is focused on industrial research into an innovative AI-based technology designed to determine the State of Health (SoH) of electric vehicle batteries during fast-charging processes. This multi-year initiative, in its first phase within this call, is dedicated to developing and validating AI algorithms capable of evaluating battery degradation in real time under real operating conditions.

The transition to a new mobility model is having a profound impact on the automotive sector, compelling companies to adapt to the production of new products—such as the shift from combustion engines to electric motors—and to incorporate advanced industrial logistics processes, such as autonomous guided vehicles. In this context, the ability to innovate and integrate new technologies and mobility models has become not only a key competitive advantage but also a critical requirement for companies to ensure survival and adapt to evolving environmental demands.