Details
| Original language | English |
|---|---|
| Pages (from-to) | 673-682 |
| Number of pages | 10 |
| Journal | Proceedings of the Design Society |
| Volume | 4 |
| Publication status | Published - 16 May 2024 |
| Event | 2024 International Design Society Conference, Design 2024 - Cavtat, Dubrovnik, Croatia Duration: 20 May 2024 → 23 May 2024 |
Abstract
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
- artificial intelligence (AI), data-driven design, design methods, optimal sensor placement, wireless sensor networks
ASJC Scopus subject areas
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Software
- Mathematics(all)
- Modelling and Simulation
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In: Proceedings of the Design Society, Vol. 4, 16.05.2024, p. 673-682.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Design methodology for optimal sensor placement for cure monitoring and load detection of sensor-integrated, gentelligent composite parts
AU - Meyer zu Westerhausen, Sören
AU - Kyriazis, Alexander
AU - Hühne, Christian
AU - Lachmayer, Roland
N1 - Publisher Copyright: © 2024 Proceedings of the Design Society. All rights reserved.
PY - 2024/5/16
Y1 - 2024/5/16
N2 - 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.
AB - 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.
KW - artificial intelligence (AI)
KW - data-driven design
KW - design methods
KW - optimal sensor placement
KW - wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=85194055862&partnerID=8YFLogxK
U2 - 10.1017/pds.2024.70
DO - 10.1017/pds.2024.70
M3 - Conference article
AN - SCOPUS:85194055862
VL - 4
SP - 673
EP - 682
JO - Proceedings of the Design Society
JF - Proceedings of the Design Society
SN - 2732-527X
T2 - 2024 International Design Society Conference, Design 2024
Y2 - 20 May 2024 through 23 May 2024
ER -