KoPf
Characteristics database and optimized production technology for small aircraft
KoPf initiates the transition from the downstream quality assurance of a static production environment to an "as-is" data-based quality optimization in a dynamic production environment and enables short innovation intervals, as well as increased performance, sustainability and profitability for CS-23 aircraft by exploiting component-specific performance.
Safety and sustainability
Seamless monitoring and documentation of production processes can reduce highly conservative safety margins. Finally, digitalization has a direct influence on the conservation of resources in production by avoiding rejects due to components that are only supposedly unusable. In addition, the full exploitation of lightweight construction potential and component-specific repair of fiber composite structures improves the carbon footprint of the operational life phase of aircraft.
Aims of KoPf
The aim of the project is to establish a seamlessly digitized manufacturing and testing infrastructure that makes it possible to guarantee a sufficiently high correlation between the properties of the raw materials, the manufacturing parameters with which the components were processed and the resulting component parameters. In detail, this means
- The end-to-end digitalized manufacturing process chains of fibre composite components
- Correlation of actually achieved component parameters with measured production parameters
- Demonstration of an approval concept based on material reference data
Method development for the predictive maintenance of structural components
Contribution to electric flying
- Full utilization of the technical certification requirements (EASA CS-23 Amendment 5)
- Reduction of reduction factors
- Improved utilization of the lightweight potential
- Increased range/payload
- Conservation of materials and resources
Analysis and optimization
- Correlation and sensitivity between semi-finished product, production and test data
- Determination of common and individual, service life-relevant parameters using machine learning
- Predictive maintenance of fiber composite components
Project data | |
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Term | 3 Years |
Participating institutes | |
