Participation in AI Roundtable in Industry: The HCPbO Project in the Emilia Romagna Region
On November 11, Fabio Esposito from TEC Eurolab, a collaborator on the HCPbO project, participated in the event "Dal dato all'azione: implementazione dell'AI nell'industria manifatturiera regionale". Organized by Clust-ER MECH and Associazione Big Data in collaboration with ART-ER and CINECA, the event was held at the Tecnopolo Manifattura in Bologna.
This gathering offered participants valuable insights into the role of artificial intelligence (AI) in manufacturing, especially regarding its potential to enhance production capacity and competitiveness within the region. The event aimed to present updated solutions, real-world use cases, and practical tools that local businesses can use in their digital transformation.
Fabio Esposito introduced the HCPbO project, which focuses on developing and implementing algorithms for optimizing industrial processes based on user preferences. This project demonstrates AI's practical application in process optimization, significantly reducing the time needed to adjust parameters while improving the quality of outcomes. The project's primary use cases include optimizing parameters for Industrial Computed Tomography (CT) with TEC Eurolab and enhancing polymer printing in a hybrid 3D printer with SMARTZAVOD.
Participants were also offered a guided tour of the LEONARDO supercomputer, one of the most powerful in Europe, managed by CINECA. This experience highlighted AI's importance in driving regional technological innovation, offering attendees a unique perspective on AI's potential in research and development.
About the HCPbO Project
The HCPbO project represents a groundbreaking solution in industrial process optimization, primarily targeting small and medium-sized enterprises (SMEs) across Europe. Combining qualitative and quantitative optimization, it offers a unique tool adaptable to various industrial applications. Key innovations include the development of hybrid algorithms that enable optimization without the need for sensor data or standard quantitative methods.