Human-AI Teaming in a Digital Twin Model for Virtual Product Development
Year: 2025
Type: article
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.
Authors: Atefeh Gooran Orimi, Sascha Saralajew, Jiatai Feng, Zhuoqun Dai, Rayen Hamlaoui, Timo Stauss, Roland Lachmayer
Institutions: Leibniz University Hannover, NEC (Germany)
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,712
OpenAlex ID: W4408922657