Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-11854
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Main Title: Neural Network Hyperparameter Optimization for the Assisted Selection of Assembly Equipment
Author(s): Hagemann, Simon
Sünnetcioglu, Atakan
Fahse, Tobias
Stark, Rainer
Type: Conference Object
Language Code: en
Abstract: The design of assembly systems has been mainly a manual task including activities such as gathering and analyzing product data, deriving the production process and assigning suitable manufacturing resources. Especially in the early phases of assembly system design in automotive industry, the complexity reaches a substantial level, caused by the increasing number of product variants and the decreased time to market. In order to mitigate the arising challenges, researchers are continuously developing novel methods to support the design of assembly systems. This paper presents an artificial intelligence system for assisting production engineers in the selection of suitable equipment for highly automated assembly systems.
URI: https://depositonce.tu-berlin.de/handle/11303/13058
http://dx.doi.org/10.14279/depositonce-11854
Issue Date: 16-Dec-2019
Date Available: 20-Apr-2021
DDC Class: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Subject(s): artificial intelligence
assembly system design
automotive
body-in-white
neural network
hyperparameter optimization
License: http://rightsstatements.org/vocab/InC/1.0/
Proceedings Title: 2019 23rd International Conference on Mechatronics Technology (ICMT)
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publisher Place: New York, NY
Publisher DOI: 10.1109/ICMECT.2019.8932099
ISBN: 978-1-7281-3998-2
Appears in Collections:FG Industrielle Informationstechnik » Publications

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