Please use this identifier to cite or link to this item: doi:10.22028/D291-37609
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Title: Combination of Human and Machine Intelligence to Optimize Assembly
Author(s): Schneider, Tizian
Klein, Steffen
Blum, Anne
Schirmer, Leonie
Müller, Rainer
Schütze, Andreas
Language: English
Title: 2019 First International Conference on Societal Automation (SA) : 2019 First International Conference on Societal Automation, September, 4-6, 2019 Krakow, Poland
Pages: 1-4
Publisher/Platform: IEEE
Year of Publication: 2019
Place of publication: Piscataway
Place of the conference: Krakow, Poland
Free key words: Machine Learning
assembly
expert knowledge
modelling
optimization
DDC notations: 600 Technology
Publikation type: Conference Paper
Abstract: Current research and futuristic approaches including machine learning promise the wide and thorough use of measurement data in assembly processes for analysis and optimization. However, in current assembly lines measurement data is not available in every process, e.g. not in manual assembly processes. In addition, the integration and combination of data from different sources within the assembly line will require huge efforts during the next years. Therefore, a solely data based approach is not suitable for current optimization projects that usually have to react quickly to occurring challenges. Thus, the research project “MessMo - metrologically supported assembly” uses, benchmarks and combines approaches from machine learning and methodic thinking for modelling, cause-effect-identification and optimization. Three approaches for modelling are utilized, accompanied by one approach for data and process optimization.
DOI of the first publication: 10.1109/SA47457.2019.8938082
URL of the first publication: https://ieeexplore.ieee.org/document/8938082
Link to this record: urn:nbn:de:bsz:291--ds-376097
hdl:20.500.11880/34024
http://dx.doi.org/10.22028/D291-37609
ISBN: 978-1-7281-3345-4
978-1-72813-346-1
Date of registration: 14-Oct-2022
Faculty: NT - Naturwissenschaftlich- Technische Fakultät
Department: NT - Systems Engineering
Professorship: NT - Prof. Dr. Andreas Schütze
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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