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Authors: Richard Nordsieck 1 ; Michael Heider 2 ; Andreas Angerer 1 and Jörg Hähner 2

Affiliations: 1 XITASO GmbH IT & Software Solutions, Augsburg and Germany ; 2 Organic Computing Group, University of Augsburg, Augsburg and Germany

ISBN: 978-989-758-380-3

Keyword(s): Additive Manufacturing, Transfer Learning, Domain Adaption, Machine Learning, Knowledge Representation.

Abstract: Commissioning of machines takes up a considerable share of time and money of the total cost of developing a machine. Our project aims at developing an approach to decrease the time needed to commission machines by automating parameter optimisation with the help of formalised expert knowledge. The approach will be developed on the Fused Deposition Modelling (FDM) process, which is an additive manufacturing technique. We pay particular attention to keeping the approach sufficiently abstract to be applied to machines from other domains to benefit its industrial application.

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Paper citation in several formats:
Nordsieck, R.; Heider, M.; Angerer, A. and Hähner, J. (2019). Towards Automated Parameter Optimisation of Machinery by Persisting Expert Knowledge.In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-380-3, pages 406-413. DOI: 10.5220/0007953204060413

@conference{icinco19,
author={Richard Nordsieck. and Michael Heider. and Andreas Angerer. and Jörg Hähner.},
title={Towards Automated Parameter Optimisation of Machinery by Persisting Expert Knowledge},
booktitle={Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2019},
pages={406-413},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007953204060413},
isbn={978-989-758-380-3},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Towards Automated Parameter Optimisation of Machinery by Persisting Expert Knowledge
SN - 978-989-758-380-3
AU - Nordsieck, R.
AU - Heider, M.
AU - Angerer, A.
AU - Hähner, J.
PY - 2019
SP - 406
EP - 413
DO - 10.5220/0007953204060413

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