Imagine if you could 3D print a complex building part or mold a car piece and have it be perfect on the first try, without any wasted materials. This system acts like a high-tech GPS for factories, using a digital twin to predict mistakes before they happen. It connects the raw material data directly to the machine settings to ensure everything fits and works exactly as planned.
Manufacturers struggle with high costs and waste when producing small batches or highly customized parts. Traditional rigid production lines cannot handle frequent changes in materials and geometries without significant downtime and errors.
A service-oriented open innovation platform featuring a Marketplace for simulation services and a digital pipeline that connects material data to production machines via IoT edge devices.
If you are a steel fabricator dealing with expensive waste in custom structural elements — this project developed a digital pipeline for Wire-Arc Additive Manufacturing (WAAM) that ensures certified path planning for a first-time-right result.
If you are a manufacturer dealing with high-mix production of Carbon Fibre Sheet Moulding Compound (CF-SMC) components — this project developed simulation-based digital twins that optimize the design and manufacturing strategy to reduce errors.
If you are an SME dealing with frequent product variants and small lot sizes — this project developed an open innovation platform with data-driven models that adapt process setpoints to production variations in real-time.
Based on available project data, specific pricing is not mentioned, but the platform includes a Marketplace to offer simulation and optimisation services.
Yes, the project aims to demonstrate the approach for enhancing the efficiency of Industrial Manufacturing Systems at TRL7.
Based on available project data, the project utilizes an Open Innovation platform and builds upon results from previous H2020 projects and associations like EMMC ASBL and IDTA.
The system integrates with production data collected from embedded IoT edge devices to update simulation-based twins.
The project period runs from 2023-01-01 to 2025-12-31.
The consortium is heavily weighted toward industrial application, with 11 industry partners representing 61% of the 18 total members. This high industry ratio, combined with 5 SMEs and partners across 11 countries, suggests the project is driven by commercial demand rather than pure academic research.
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