PhoenixD

E-Mail:  stucki@match.uni-hannover.de
Team:  Martin Stucki, Rolf Wiemann, Niklas Terei, Lars Binnemann
Year:  2019
Funding:  DFG
Further information https://www.phoenixd.uni-hannover.de/en/

Complicated, multi-stage processes with very high accuracy requirements characterize optics production. Increased numbers of variants and small quantities require flexible and adaptive production systems. For the optics production of the future, PhoenixD is therefore aiming to optimize not only the process parameters statically during production but also the entire component function in real-time. The individual properties of each component are mapped from measurement and simulation data in a virtual model during the process.

An integrated production network is being developed in PhoenixD to produce newly developed optical systems. In an inline production system, various manufacturing processes, such as coating, printing, and assembly, are linked with inline measurement systems. In this context, the match investigates a novel assembly concept using a magnetic levitation system for workpiece transport between the individual production processes and simultaneous precise component handling.

Furthermore, research is being conducted into new strategies for assembling photonically integrated systems. Novel process solutions should enable adaptive, efficient and functionally optimized assembly. One approach is self-assembly, whereby components position themselves through a particular component design, eliminating the need for a precise handling system. In addition to developing assembly concepts and corresponding hardware, the focus is particularly on using process and simulation data from the entire development and production chain. The combination of simulation, development and production data of the optical systems developed in PhoenixD in a uniform data format should enable both a prediction of the assembly quality and self-optimizing assembly systems.

Contact: Martin Stucki, Niklas Terei, Lars Binnemann, Rolf Wiemann

Concept of Predictive Quality Assembly