Specific challenge:Production quality significantly depends on the ambient conditions and the process parameters. Computational models capable of simulating the machine-to-part process not only can be used to predict manufacturing quality and productivity but, increasingly, to also compensate wear or partial damage through model-based control. Innovative machines and processes increasingly depend on model-based approaches, including the monitoring and control elements, throughout the whole machine lifecycle.
New integrated approaches are needed in simulation methods, tools and across hierarchical model layers requiring a cross-disciplinary collaboration between (predominantly SME) machine designers, industrial component suppliers, engineering software developers as well as making use of the process experience of manufacturers.
Scope: RTD and innovation activities should aim at developing and testing suitable model-based approaches for production machinery and at demonstrating the power of model-driven approaches for machine innovation through:
- The development of integrated and accurate simulation models and algorithms for model-based control of production machinery based on cross-disciplinary input and actual machine lifecycle parameters.
- Tool programming strategies that are easy to use and can be rapidly modified or re-adapted by workers on the machine.
- Demonstration of the reliability of model-based machines with respect to production accuracy/quality, maintainability and lifecycle return-on-investment (e.g. through an industrially scalable demonstrator).
Activities expected to focus on Technology Readiness Levels 4-6.
The Commission considers that proposals requesting a contribution from the EU between EUR 3 and 6 million would allow this specific challenge to be addressed appropriately. Nonetheless, this does not preclude submission and selection of proposals requesting other amounts.
Improved system adaptability and reduction of lifecycle costs by 30% for manufacturing system and process.
New maintainability concepts based on predictive ""(self-) maintenance"" with machine reliability improved by 10% (MTBF) and reduced maintenance costs by 20%.
In terms of environmental impact: Reduced waste and energy efficiency improved by 30%.
Type of action: Research & Innovation Actions