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| Funder | Strategic Priorities Fund |
|---|---|
| Recipient Organization | I3D Robotics Ltd |
| Country | United Kingdom |
| Start Date | Dec 01, 2023 |
| End Date | Nov 30, 2025 |
| Duration | 730 days |
| Data Source | UKRI Gateway to Research |
| Grant ID | 10080713 |
**_Robotic Fabrication of Tunnel Services using Industry 4.0 for Intelligent Distributed Manufacturing (_**_RoFab-4iDM) addresses safety and productivity problems that occur when fabricating customised mechanical and electrical (M&E) installation bracketry within rail and infrastructure tunnels._
**_RoFab-4iDM will radically improve productivity and worker safety by integrating survey, inspection, machining and additive manufacturing operations within an integrated, autonomous "survey-\>design-\>manufacture-\>install" system._** **_South Korean partners will apply this technology automating metal pipework repairs within tunnels._**
We will combine conventional test techniques with machine learning (ML) and artificial intelligence (AI) to create a digital control environment capable of assessing and designing welds, fabrication, machining and additive manufacturing without human decision-making.
**_Innovating to solve real-world problems: Improve worker safety; saving UK taxpayers £16,000 per km_ tunnel built.**
RoFab-4iDM has immediate application for tunnelling projects including HS2, capturing benefits of autonomous robotics, while forming tunnel structures using conventional, cost-effective, precast concrete (PCC). No other system of this kind is available. **The issue:**
Rail tunnel M&E services are fitted to racking bolted to inserts cast into PCC tunnel sections. Installation tolerance build-up (due to 'roll' of PCC sections) means inserts are often out of position. Safety rules forbid welding and deter drilling new inserts inside tunnels. Therefore, many brackets need bespoke adaptions.
**Our proposal:**
RoFab-4iDM will combine tunnel design models with as-constructed 3D survey data, using AI to automate design and manufacture of customised bracketry. Each bracket will precisely suit the as-installed inserts, enabling fast robotic installation.
* **Precisely determine as-installed position of each insert** - automated scanning rig survey creates digital 3D representation. * **Position-specific, customised standard bracket design** created by AI using digital twin. * **Fabricate bracketry offsite** -- autonomous, adaptive robotic fabrication.
The project integrates proven techniques/technologies with a novel digital twin. The demonstrator will develop the digital twin integration from TRL5 to TRL7\. **Further markets:**
**_Enhancing construction productivity by creating a method for off-site mass manufactured components that are cost-efficient and bespoke._**
Bridging the variance between designed ideal/predicted dimensions and as-installed would accelerate wide adoption of pre-manufactured products on construction projects.
**_Autonomous repair to regulated standards within hazardous environments._** Applying RoFab-4iDM technologies to other sectors will provide a solution for welding/AM within hazardous environments, avoiding disruption from having to move damaged components to a repair workshop. Future development would be to combine all stages into a **survey-\>design-\>manufacture-\>install** platform, with applications in any industry that requires assessment, repair and remanufacture of steel materials, including rail and civil nuclear decommissioning.
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