Model-based increase of flexibility and robustness of an aerodynamic part feeding system for high-performance assembly

• Applicants: Professor Dr.-Ing. Peter Nyhuis; Professorin Dr.-Ing. Annika Raatz
• Subject Area: Production Systems, Operations Management, Quality Management and Factory Planning ; Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics
• Term: from 2013 to 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 243351293

To counter existing deficits of conventional feeding systems regarding feeding performance, reliability and variant flexibility, an aerodynamic part feeding system has been developed at the IFA. This system is particularly characterized by its feeding rate of up to 1,000 workpieces per minute and its high technical availability. The setting-up of the part feeding system to different workpieces is limited to the adaption of only four system parameters. However, the identification of optimal parameter values was very time-consuming. In previous research activities, therefore, a genetic algorithm has been developed. This algorithm enables the part feeding system to autonomously identify optimal parameter values and to adjust them automatically via corresponding hardware. As a consequence, the time to adjust the part feeding system could be reduced significantly. In this subsequent research proposal, the spectrum of workpieces to be feeded should be expanded by adapting the genetic algorithm and the part feeding system in order to meet the challenges of increasing product individualization. Nowadays, the simulation of different workpiece geometries necessitates manual adjustments in the simulation model. This requires the existence of corresponding simulation experts. Therefore, an aim is to extend the simulation model in a way that enables the simulation of different workpieces without the need to adjust the simulation manually. Furthermore, the simulation model should be extended and optimized in a way that its results lead to a reduction of time to identify optimal parameter values in practice. For being able to feed a wider spectrum of workpieces without decreasing the feeding rate, optimal settings of the genetic algorithm (e. g. mutation rate or population size) should be identified as a function of the characteristics of the workpieces to be feeded and an economic triggering of a reparameterization when ambient conditions change should be developed. Thereby, the spectrum of workpieces to be feeded is to be defined by a catalog of relevant workpiece characteristics and their associated limits.

• DFG Programme: Research Grants