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A genetic algorithm for a self-learning parameterization of an aerodynamic part feeding system for high-speed assembly

Research output: Journal contributions › Journal articles › Research › peer-review

Standard

A genetic algorithm for a self-learning parameterization of an aerodynamic part feeding system for high-speed assembly. / Busch, Jan; Quirico, Melissa; Richter, Lukas et al.
In: CIRP Annals - Manufacturing Technology, Vol. 64, No. 1, 2015, p. 5-8.

Research output: Journal contributions › Journal articles › Research › peer-review

Harvard

Busch, J, Quirico, M, Richter, L, Schmidt, M, Raatz, A & Nyhuis, P 2015, 'A genetic algorithm for a self-learning parameterization of an aerodynamic part feeding system for high-speed assembly', CIRP Annals - Manufacturing Technology, vol. 64, no. 1, pp. 5-8. https://doi.org/10.1016/j.cirp.2015.04.044

APA

Busch, J., Quirico, M., Richter, L., Schmidt, M., Raatz, A., & Nyhuis, P. (2015). A genetic algorithm for a self-learning parameterization of an aerodynamic part feeding system for high-speed assembly. CIRP Annals - Manufacturing Technology, 64(1), 5-8. https://doi.org/10.1016/j.cirp.2015.04.044

Vancouver

Busch J, Quirico M, Richter L, Schmidt M, Raatz A, Nyhuis P. A genetic algorithm for a self-learning parameterization of an aerodynamic part feeding system for high-speed assembly. CIRP Annals - Manufacturing Technology. 2015;64(1):5-8. doi: 10.1016/j.cirp.2015.04.044

Bibtex

@article{7607604e84b04f6fb88ca1e31e9bf425,
title = "A genetic algorithm for a self-learning parameterization of an aerodynamic part feeding system for high-speed assembly",
abstract = "The aerodynamic feeding technology developed at the IFA allows feeding rates up to 800 parts per minute while maintaining high reliability and variant flexibility. The machine's setup procedure requires the adaptation of only four machine parameters. Currently, optimal parameter configurations need to be identified manually. This task is greatly time-consuming and requires a high level of expertise. Prospectively, the machine should utilize an algorithm that autonomously identifies optimal parameter configurations for new workpieces to realize fast setup procedures. This paper presents a genetic algorithm for a self-learning feeding system that has been validated in comprehensive simulation studies.",
keywords = "Algorithm, Assembly, Optimization, Engineering",
author = "Jan Busch and Melissa Quirico and Lukas Richter and Matthias Schmidt and Annika Raatz and Peter Nyhuis",
year = "2015",
doi = "10.1016/j.cirp.2015.04.044",
language = "English",
volume = "64",
pages = "5--8",
journal = "CIRP Annals - Manufacturing Technology",
issn = "0007-8506",
publisher = "Elsevier Inc.",
number = "1",

}

RIS

TY - JOUR

T1 - A genetic algorithm for a self-learning parameterization of an aerodynamic part feeding system for high-speed assembly

AU - Busch, Jan

AU - Quirico, Melissa

AU - Richter, Lukas

AU - Schmidt, Matthias

AU - Raatz, Annika

AU - Nyhuis, Peter

PY - 2015

Y1 - 2015

N2 - The aerodynamic feeding technology developed at the IFA allows feeding rates up to 800 parts per minute while maintaining high reliability and variant flexibility. The machine's setup procedure requires the adaptation of only four machine parameters. Currently, optimal parameter configurations need to be identified manually. This task is greatly time-consuming and requires a high level of expertise. Prospectively, the machine should utilize an algorithm that autonomously identifies optimal parameter configurations for new workpieces to realize fast setup procedures. This paper presents a genetic algorithm for a self-learning feeding system that has been validated in comprehensive simulation studies.

AB - The aerodynamic feeding technology developed at the IFA allows feeding rates up to 800 parts per minute while maintaining high reliability and variant flexibility. The machine's setup procedure requires the adaptation of only four machine parameters. Currently, optimal parameter configurations need to be identified manually. This task is greatly time-consuming and requires a high level of expertise. Prospectively, the machine should utilize an algorithm that autonomously identifies optimal parameter configurations for new workpieces to realize fast setup procedures. This paper presents a genetic algorithm for a self-learning feeding system that has been validated in comprehensive simulation studies.

KW - Algorithm

KW - Assembly

KW - Optimization

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=84933677914&partnerID=8YFLogxK

U2 - 10.1016/j.cirp.2015.04.044

DO - 10.1016/j.cirp.2015.04.044

M3 - Journal articles

AN - SCOPUS:84933677914

VL - 64

SP - 5

EP - 8

JO - CIRP Annals - Manufacturing Technology

JF - CIRP Annals - Manufacturing Technology

SN - 0007-8506

IS - 1

ER -

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