Simone Luca Pizzagalli

The research project focuses on Industry 4.0/5.0 digital production technologies, which enable the development of new products also considering their production readiness to be significantly accelerated. The whole life cycle of the product is under consideration, from the creation of digital product models using 3D scanning, digital twins, and simulation technologies; rapid product prototyping through additive manufacturing technologies to integrate both mechanics and electronics; production based on the principles of lean manufacturing, quality control and product and production monitoring. As a result of the project, a prototyping development and demo centre/ experimental lab will be created in Virumaa College, which enables the development and production of complex and smart mechatronic products.

AIM-Labs addresses educational gap by creating a new generation of AI-Driven Smart Manufacturing Learning Labs that blend data, simulation, and extended reality (XR). The project introduces AI SPRINTS—structured, challenge-based learning pathways in which students collaboratively design, prototype, and evaluate AI-enabled solutions for realistic manufacturing scenarios. These sprints integrate multiple technological layers, from data preparation and machine learning to discrete-event simulation, optimisation, digital twins, and XR-based interaction. The project is driven by the conviction that engineering students learn best when they can manipulate real or realistic data, test alternatives in simulation, and visualise system behaviour through immersive and interactive environments. By embedding AI SPRINTS into higher-education curricula, AIM-Labs will empower learners to develop the competencies required in the emerging AI-enhanced manufacturing workplace: data literacy, model development and validation, human-in-the-loop decision-making, explain-ability awareness, and the ability to evaluate trade-offs in complex production systems. The project aims to: - Deliver and validate AI-driven learning SPRINTS in higher education programs in industrial and manufacturing engineering. - Establish AI SPRINT co-design labs where students apply and evaluate AI solutions in data analytics, simulation, and XR. - Foster collaborative, challenge-based learning through immersive workshops integrating AI, digital manufacturing, and XR technologies. - Enhance students’ problem-solving skills, practical competencies, and innovation readiness.