The EU-project COGNIMAN aims to improve difficult-to-automate manufacturing processes, such as glass fibre production, precision machining, additive manufacturing and high-temperature metal production, which currently require significant labour-intensive efforts. These manufacturing processes are all difficult to automate as the tasks involved are complex, and cannot be fully controlled.
Our goal is to develop and demonstrate a new concept of “digital cognitive smart manufacturing” that integrates advanced technologies like simulations, digital twins, advanced sensors, machine learning and cognitive robotics.
This approach will enable flexible and efficient manufacturing towards zero waste and high-quality products, ultimately boosting the European technology and manufacturing sectors’ competitiveness in global markets. It will also help reduce the environmental footprint of manufacturing activities.
COGNIMAN encompasses a well-balanced consortium of sixteen partners from seven European nations. NORCE will coordinate the work.
The four pilots (use-cases)
Exploitable demonstrators and prototypes will be developed and tested in the four designated project pilots.
- Defect detection in fibreglass production
Glass fibres are produced from molten glass, but breaks in the fibres disrupt production and are hard to predict. COGNIMAN aims to automate break detection using sensors and machine learning, improving operator response, reducing waste and lowering the production’s carbon footprint. - Precision machining – deburring of large metal parts
A burr is excess material left on a part’s edge after manufacturing. Deburring removes this material. For large part manufacturing, COGNIMAN aims to develop a robotic solution to automate deburring using machine learning and sensors, improving part quality, increasing efficiency and reducing physically strenuous work for operators. - Additive Manufacturing of medical implants
Medical implants made with additive manufacturing require highly sophisticated post-processing to make sure no sharp edges remain which could potentially damage tissue during implantation. Complexity increases with patient-specific implants since every person has a unique geometry. COGNIMAN aims to develop algorithms to guide robots to automate this process while maintaining high-quality finishes. The result would be a safe, flexible robot system that collaborates seamlessly with the operators. - Flexible manufacturing – digital library for batches
How to operate and maintain a large outdoor site with finished and semi-finished products in batches or large variety, size and dimension in steel manufacturing? This is the challenge COGNIMAN seeks to solve. COGNIMAN aims to establish a digital library that provides a real-time overview and will thus allow full visibility of stock and internal logistics processes alike.
The project is co-funded by the Eurpean Union.
