Details
| Titel in Übersetzung | Mensch-KI-Zusammenarbeit in einem Digitalen Zwillingsmodell für die virtuelle Produktentwicklung |
|---|---|
| Originalsprache | Englisch |
| Seiten (von - bis) | 76-84 |
| Seitenumfang | 9 |
| Fachzeitschrift | ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb |
| Jahrgang | 120 |
| Ausgabenummer | s1 |
| Publikationsstatus | Veröffentlicht - 27 März 2025 |
Abstract
Digital Twin Models (DTMs) are key to Industry 4.0, improving manufacturing and product development through AI. They enable real-time monitoring, predictive maintenance, and optimization across the product lifecycle. However, none human-comprehensible AI algorithms, especially deep neural networks, present challenges. In this regard, this study explores human-AI collaboration to create an AI-driven DTM for virtual product development. We propose a DTM that combines AI and expert knowledge to enable informed decision-making for product design optimization. For this, the architecture and functionality of the developed model will be first outlined, and further, the application of the model will be demonstrated in analyzing LiDAR systems.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Allgemeiner Maschinenbau
- Betriebswirtschaft, Management und Rechnungswesen (insg.)
- Strategie und Management
- Entscheidungswissenschaften (insg.)
- Managementlehre und Operations Resarch
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in: ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb, Jahrgang 120, Nr. s1, 27.03.2025, S. 76-84.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Human-AI Teaming in a Digital Twin Model for Virtual Product Development
AU - Orimi, Atefeh Gooran
AU - Saralajew, Sascha
AU - Feng, Jiatai
AU - Dai, Zhuoqun
AU - Hamlaoui, Rayen
AU - Stauss, Timo
AU - Lachmayer, Roland
N1 - Publisher Copyright: © 2025 Atefeh Gooran Orimi, Sascha Saralajew, Jiatai Feng, Zhuoqun Dai, Rayen Hamlaoui, Timo Stauss and Roland Lachmayer, publiziert von De Gruyter.
PY - 2025/3/27
Y1 - 2025/3/27
N2 - Digital Twin Models (DTMs) are key to Industry 4.0, improving manufacturing and product development through AI. They enable real-time monitoring, predictive maintenance, and optimization across the product lifecycle. However, none human-comprehensible AI algorithms, especially deep neural networks, present challenges. In this regard, this study explores human-AI collaboration to create an AI-driven DTM for virtual product development. We propose a DTM that combines AI and expert knowledge to enable informed decision-making for product design optimization. For this, the architecture and functionality of the developed model will be first outlined, and further, the application of the model will be demonstrated in analyzing LiDAR systems.
AB - Digital Twin Models (DTMs) are key to Industry 4.0, improving manufacturing and product development through AI. They enable real-time monitoring, predictive maintenance, and optimization across the product lifecycle. However, none human-comprehensible AI algorithms, especially deep neural networks, present challenges. In this regard, this study explores human-AI collaboration to create an AI-driven DTM for virtual product development. We propose a DTM that combines AI and expert knowledge to enable informed decision-making for product design optimization. For this, the architecture and functionality of the developed model will be first outlined, and further, the application of the model will be demonstrated in analyzing LiDAR systems.
KW - Artificial Intelligence
KW - Digital Twin Model
KW - Human-AI Teaming
KW - Product Design
KW - Product Development
UR - http://www.scopus.com/inward/record.url?scp=105004686224&partnerID=8YFLogxK
U2 - 10.1515/zwf-2024-0141
DO - 10.1515/zwf-2024-0141
M3 - Article
AN - SCOPUS:105004686224
VL - 120
SP - 76
EP - 84
JO - ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb
JF - ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb
SN - 0947-0085
IS - s1
ER -