Implementation and Testing of a Genetic Algorithm for a Self-learning and Automated Parameterisation of an Aerodynamic Feeding System | Zendy

Jan Busch | Zendy; Sebastian Blankemeyer | Zendy; Annika Raatz | Zendy; Peter Nyhuis | Zendy

Open Access

Implementation and Testing of a Genetic Algorithm for a Self-learning and Automated Parameterisation of an Aerodynamic Feeding System

Author(s) -

Jan Busch,

Sebastian Blankemeyer,

Annika Raatz,

Peter Nyhuis

Publication year - 2016

Publication title -

procedia cirp

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.683

H-Index - 65

ISSN - 2212-8271

DOI - 10.1016/j.procir.2016.02.081

Subject(s) - aerodynamics , genetic algorithm , convergence (economics) , reliability (semiconductor) , algorithm , rate of convergence , identification (biology) , computer science , control theory (sociology) , control (management) , engineering , artificial intelligence , machine learning , biology , ecology , key (lock) , power (physics) , physics , quantum mechanics , aerospace engineering , economics , economic growth , computer security

An active aerodynamic feeding system developed at the IFA offers a large potential regarding output rate, reliability and neutrality towards part geometries. In this paper, the procedure of a genetic algorithm's into the feeding system's control is shown. The genetic algorithm automatically identifies optimal values for the feeding system's parameters which need to be adjusted when setting up for new workpieces. The general functioning of the automatic parameter identification is confirmed during tests on the convergence behaviour of the genetic algorithm. Thereby, a trade-off between the adjustment time of the feeding system and the solution quality is revealed

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research