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Design methodology for optimal sensor placement for cure monitoring and load detection of sensor-integrated, gentelligent composite parts

Published online by Cambridge University Press:  16 May 2024

Sören Meyer zu Westerhausen ,
Alexander Kyriazis ,
Christian Hühne  and
Roland Lachmayer
Sören Meyer zu Westerhausen*
Affiliation:
Leibniz University Hannover, Germany
Alexander Kyriazis
Affiliation:
Technische Universität Braunschweig, Germany
Christian Hühne
Affiliation:
Technische Universität Braunschweig, Germany
Roland Lachmayer
Affiliation:
Leibniz University Hannover, Germany
*
meyer-zu-westerhausen@ipeg.uni-hannover.de

Abstract

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Selecting right positions for composite-integrated sensors for monitoring cure during manufacturing and loads during product use presents challenges for engineering design. Since an optimal sensor placement (OSP) methodology for both phases is not emphasised enough in literature, a new methodology is proposed. This methodology is based on a Genetic Algorithm and strain gauges, temperature sensors and interdigitated electrode sensors for cure monitoring and physics-informed neural network-based load detection. Additionally, it includes sensor node positions optimization in a sensor network.

Keywords

design methodsdata-driven designoptimal sensor placementwireless sensor networksartificial intelligence (AI)
Type
Design Methods and Tools
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 673 - 682
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

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