A genetic algorithm for a self-learning parameterization of an aerodynamic part feeding system for high-speed assembly
Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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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, Jahrgang 64, Nr. 1, 2015, S. 5-8.Publikation: Beiträge in Zeitschriften › Zeitschriftenaufsätze › Forschung › begutachtet
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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 -