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doi:10.22028/D291-37609
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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