Abstract
<div class="section abstract"><div class="htmlview paragraph">Digital twin technology has become impactful in Industry 4.0 as it enables engineers to design, simulate, and analyze complex systems and products. As a result of the synergy between physical and virtual realms, innovation in the “real twin” or actual product is more effectively fostered. The availability of verified computer models that describe the target system is important for realistic simulations that provide operating behaviors that can be leveraged for future design studies or predictive maintenance algorithms. In this paper, a digital twin is created for an offroad tracked vehicle that can operate in either autonomous or remote-control modes. Mathematical models are presented and implemented to describe the twin track and vehicle chassis governing dynamics. These components are interfaced through the nonlinear suspension elements and distributed bogies. The assembled digital twin’s performance was investigated using test data collected from the Clemson University Deep Orange 13/14 tracked vehicles. The prototype vehicle completed a series of operating scenarios with both on-board data collection and video monitoring to document the performance. Similar scenarios were emulated in the digital twin virtual environment. Representative numerical and field results will be collectively presented to demonstrate the performance of the digital twin in estimating the real twin behavior. This virtual tool, with coupling option to/from the physical system, establishes a foundation for predictive maintenance and next generation vehicle design studies.</div></div>
Reference21 articles.
1. Jiao , R. , Commuri , S. , Panchal , J. , Milisavljevic-Syed , J.J. et al. Design Engineering in the Age of Industry 4.0 Journal of Mechanical Design 143 7 2021 10.1115/1.4051041
2. Tuegel , E.J. Reengineering Aircraft Structural Life Prediction Using a Digital Twin International Journal of Aerospace Engineering October 2011 2011 10.1155/2011/154798
3. Erol , T. , Mendi , A.F. , and Dogan , D. 2020 10.1109/ISMSIT50672.2020.9255249
4. Procopio , D. , Morris , J. , and Wagner , J. June 2023
5. Han , T. and Kaushik , S. Development of Computer Aided Design Tools for Automotive Batteries Computer Aided Engineering of Batteries Switzerland Springer Link AG 2023 119 186
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. A Reinforcement Learning-based Adaptive Digital Twin Model for Forests;2024 4th International Conference on Applied Artificial Intelligence (ICAPAI);2024-04-16