Imagine having a digital ghost of an ancient clock or bridge that doesn't just look like the real thing, but actually moves and feels the same. By using special scanners and sensors, it can predict when a part is about to break before it actually happens. It's like having a health monitor for old machines to keep them running forever.
Maintaining mechanical cultural heritage is risky and expensive because internal wear is often invisible until a catastrophic failure occurs. There is currently no standardized way to simulate the movement and stress of these unique objects digitally.
A 4D digital twin system that combines imaging, IoT sensors, and AI for predictive maintenance and functional simulation of mechanical heritage objects.
If you are a museum operator dealing with the decay of historic machinery—this project developed a 4D digital twin that allows for predictive failure analysis. This means you can prevent costly breakdowns of unique assets like looms or clock towers. It also creates immersive experiences for visitors.
If you are a conservation firm dealing with complex structural repairs—this project developed a system using muography and laser scanning that captures internal complexity. This allows you to simulate mechanical performance and strain without touching the fragile object. It reduces the risk of damaging heritage assets during restoration.
If you are a tech company dealing with a lack of standardized heritage data—this project developed a DTaaS (Digital Twin as a Service) marketplace. This allows you to offer and consume specialized services through a secure interface. It aligns with the ECCCH framework for better data interoperability.
Based on available project data, specific pricing is not mentioned, but the project introduces a 'Marketplace' and 'DTaaS' (Digital Twin as a Service) model for third-party providers.
Yes, the project validates its technology using representative case studies of large-scale objects such as clock towers and drawbridges.
Based on available project data, the project emphasizes a secure interface for third-party service providers to offer and consume services, though specific licensing terms are not listed.
The platform is designed to be seamlessly aligned with the ECCCH framework to ensure data interoperability.
The project is scheduled to run from 2026-01-01 to 2028-12-31.
The consortium is well-balanced for commercialization, featuring 18 partners across 11 countries. With an industry ratio of 44% (8 companies, including 7 SMEs), there is a strong bridge between academic research and market application, reducing the risk of developing purely theoretical tools.
Contact Universidad de Salamanca for partnership opportunities.
Contact us to find a partner in the KINETIKA consortium for DTaaS integration.
