The project focuses on two representative processes – expanded metal production and turn-pressing – where variations in materials, tools and machine behaviour currently result in scrap, rework and increased energy and material consumption. The solutions are demonstrated and validated in a pilot environment at the industrial partners’ sites at TRL 6.
Expected outcomes include shorter start-up times, reduced material waste, improved resource efficiency and lower energy consumption in small and medium-sized manufacturing companies. The results are disseminated via industrial networks and scientific publications, and are integrated into engineering degree programmes to build long-term expertise.
Project Objective
The project aims to develop and industrially validate AI-based decision support for sheet metal and metal forming by integrating production data, material properties, tool condition and machine behaviour into predictive and prescriptive models.
Project objectives
By the end of the project, the following should have been achieved:
- An industrially validated AI-based decision support function for two forming processes.
- Demonstrated ability to predict quality, stability/variability and energy consumption, and to provide recommendations for machine settings.
- Quantified improvements in set-up time, scrap/rework and resource utilisation compared with established baselines.
- A selected and documented modelling approach that meets requirements for robustness, transparency
and integrability into existing workflows.
