Flexible SDM through Continuously Quality-Aware Digital Twins
The vision of Software- Defined Manufacturing (SDM) and Industry 4.0 is an industrial production that reacts flexibly to rapidly changing markets. This requires highly adaptive, versatile production systems.
For this purpose, process and machine states or the capabilities of the systems to be adapted must be precisely recorded. Up to now, the quality of process execution (and thus of the products) and machine capabilities have only been recorded "statically", i.e. under fixed process and framework conditions, so that quality predictions are only made on the basis of idealized processes and results. Since machine and process behavior is constantly changing during operation, discrepancies occur between idealized process quality predictions and actual real process behavior.
To solve this problem, this project conducts research on self-adaptive digital twins of production systems that make process-dependent, accurate quality predictions using autonomously learned capabilities and qualities.