To main content

COGNIMAN - COGNitive Industries for smart MANufacturing

The project aims to address the need for flexible and efficient manufacturing that produces zero waste and high-quality products.

Contact person

Automation of processes involved with glass fibre production, precision machining of large parts (e.g., wind turbines), additive manufacturing of medical implants and high-temperature metal production are manufacturing examples with processes offer several challenges.

The main reasons for the actual labor-intensive efforts in these scenarios are lack of full understanding and control over the individual manufacturing steps and the high complexity of the tasks. This has severe impacts on sustainable growth, manufacturing productivity, efficiency, and flexibility due to the large amount of unpredictive waste in production and processing time.

COGNIMAN intends to solve these challenging situations by developing and demonstrating a novel concept of digital cognitive smart manufacturing that will shift the future design of manufacturing processes towards autonomous and predictive manufacturing with improved flexibility, safety and efficiency.

SINTEF’s Role and Contribution

As an R&D partner, SINTEF strives on contributing to value creation and increased competitiveness within the public and private sectors. COGNIMAN gives us the opportunity to work in the emerging area of cognitive manufacturing pilots with a highly competent project consortium.

SINTEF is leading tasks related to sensors' suites for manufacturing, simulation framework and standards monitoring. In addition, it will be actively involved in evaluating and preparing cognitive manufacturing pilots. The project involves the use of field trials and development of software.

SINTEF is participating in COGNIMAN with researchers from the following Institutes:

  • SINTEF Industry (Departments: Process Technology, Metal Production and Processing)
  • SINTEF Digital (Departments: Smart Sensors and Microsystems, Sustainable Communication Technologies)

Key Factors

Website

www.cogniman.eu

Funding

This project has received funding from the European Union’s HORIZON-CL4-2021-TWIN-TRANSITION-01 research and innovation programme under grant agreement No 101058477.

Project duration

2023 - 2026