Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 79-84 |
| Seitenumfang | 6 |
| Fachzeitschrift | Procedia CIRP |
| Jahrgang | 44 |
| Publikationsstatus | Veröffentlicht - 11 Mai 2016 |
| Veranstaltung | 6th CIRP Conference on Assembly Technologies and Systems, CATS 2016 - Gothenburg, Schweden Dauer: 16 Mai 2016 → 18 Mai 2016 |
Abstract
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.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
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in: Procedia CIRP, Jahrgang 44, 11.05.2016, S. 79-84.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Implementation and Testing of a Genetic Algorithm for a Self-learning and Automated Parameterisation of an Aerodynamic Feeding System
AU - Busch, Jan
AU - Blankemeyer, Sebastian
AU - Raatz, Annika
AU - Nyhuis, Peter
N1 - Funding information: The authors would like to thank the German Research Foundation (DFG) for their financial support of the research project NY 4/51-1.
PY - 2016/5/11
Y1 - 2016/5/11
N2 - 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.
AB - 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.
KW - Algorithm
KW - Assembly
KW - Optimisation
UR - http://www.scopus.com/inward/record.url?scp=84994120841&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2016.02.081
DO - 10.1016/j.procir.2016.02.081
M3 - Conference article
AN - SCOPUS:84994120841
VL - 44
SP - 79
EP - 84
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
T2 - 6th CIRP Conference on Assembly Technologies and Systems, CATS 2016
Y2 - 16 May 2016 through 18 May 2016
ER -