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

Research output: Contribution to journalConference articleResearchpeer review

Authors

View graph of relations

Details

Original languageEnglish
Pages (from-to)79-84
Number of pages6
JournalProcedia CIRP
Volume44
Publication statusPublished - 11 May 2016
Event6th CIRP Conference on Assembly Technologies and Systems, CATS 2016 - Gothenburg, Sweden
Duration: 16 May 201618 May 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.

Keywords

    Algorithm, Assembly, Optimisation

ASJC Scopus subject areas

Cite this

Implementation and Testing of a Genetic Algorithm for a Self-learning and Automated Parameterisation of an Aerodynamic Feeding System. / Busch, Jan; Blankemeyer, Sebastian; Raatz, Annika et al.
In: Procedia CIRP, Vol. 44, 11.05.2016, p. 79-84.

Research output: Contribution to journalConference articleResearchpeer review

Busch J, Blankemeyer S, Raatz A, Nyhuis P. Implementation and Testing of a Genetic Algorithm for a Self-learning and Automated Parameterisation of an Aerodynamic Feeding System. Procedia CIRP. 2016 May 11;44:79-84. doi: 10.1016/j.procir.2016.02.081, 10.15488/1046
Download
@article{0361911d4da4407889a26d997e936fd3,
title = "Implementation and Testing of a Genetic Algorithm for a Self-learning and Automated Parameterisation of an Aerodynamic Feeding System",
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.",
keywords = "Algorithm, Assembly, Optimisation",
author = "Jan Busch and Sebastian Blankemeyer and Annika Raatz and Peter Nyhuis",
note = "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.; 6th CIRP Conference on Assembly Technologies and Systems, CATS 2016 ; Conference date: 16-05-2016 Through 18-05-2016",
year = "2016",
month = may,
day = "11",
doi = "10.1016/j.procir.2016.02.081",
language = "English",
volume = "44",
pages = "79--84",

}

Download

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 -

By the same author(s)