THE USE OF SIMULATION WITH MACHINE LEARNING AND OPTIMIZATION FOR A DIGITAL TWIN- A CASE ON FORMULA 1 DSS

被引：3

作者：

Greasley, Andrew ^{[1
]}

Panchal, Gajanan ^{[2
]}

Samvedi, Avinash ^{[3
]}

机构：

[1] Aston Univ, Coll Engn & Phys Sci, Dept Engn Syst & Supply Chain Management, Birmingham B4 7ET, England

[2] Aston Univ, Coll Business & Social Sci, Dept Operat & Informat Management, Birmingham B4 7ET, England

[3] Natl Univ Singapore, Dept Ind Syst Engn & Management, 1 Engn Dr 2,Block E1A,06-25, Singapore 117576, Singapore

来源：

2022 WINTER SIMULATION CONFERENCE (WSC) | 2022年

关键词：

STRATEGIES; DESIGN;

D O I：

10.1109/WSC57314.2022.10015299

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The implementation of a digital twin presents a challenging environment for simulation. One challenge is the need for fast execution speed to maintain synchronization with the real system. When providing predictive outcomes, the complementary use of simulation with machine learning and optimization software may be employed to achieve this aim. The article investigates the use of simulation, machine learning and optimization in terms of providing a digital twin capability. The article presents a case on Formula1 or F1 competition, where a decision support system (DSS) framework is presented to explore a digital twin capability.

引用

页码：2198 / 2209

页数：12

共 50 条

[1] Machine Learning Enabled FBAR Digital Twin for Rapid Optimization
Simon, Gergely
Hantos, Gergely B.
Patel, Mihir S.
Tweedie, Andrew
Harvey, Gerald
PROCEEDINGS OF THE 2020 IEEE INTERNATIONAL ULTRASONICS SYMPOSIUM (IUS), 2020,
[2] Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry
Min, Qingfei
Lu, Yangguang
Liu, Zhiyong
Su, Chao
Wang, Bo
INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT, 2019, 49 : 502 - 519
[3] Combining Simulation and Machine Learning as Digital Twin for the Manufacturing of Overmolded Thermoplastic Composites
Huerkamp, Andre
Gellrich, Sebastian
Ossowski, Tim
Beuscher, Jan
Thiede, Sebastian
Herrmann, Christoph
Droeder, Klaus
JOURNAL OF MANUFACTURING AND MATERIALS PROCESSING, 2020, 4 (03):
[4] A digital twin framework for machine learning optimization of aerial fire fighting and pilot safety
Zohdi, T. I.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2021, 373
[5] Digital Twin simulation models: a validation method based on machine learning and control charts
dos Santos, Carlos Henrique
Campos, Afonso Teberga
Montevechi, Jose Arnaldo Barra
Miranda, Rafael de Carvalho
Costa, Antonio Fernando Branco
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2024, 62 (07) : 2398 - 2414
[6] Digital Twin for Flexible Manufacturing Systems and Optimization Through Simulation: A Case Study
Florescu, Adriana
MACHINES, 2024, 12 (11)
[7] Use of a digital twin for process optimization and predictive maintenance using the machine tools as example
Schmucker B.
Ellinger J.
Benker M.
Semm T.
Zäh M.F.
1600, Carl Hanser Verlag, Kolbergerstrasse 22, Munchen, D-81679, Germany (115): : 78 - 83
[8] Identification of a Practical Digital Twin for Simulation of Machine Tools
Fujita, Tomoya
Xi, Tiandong
Ikeda, Ryosuke
Kehne, Sebastian
Fey, Marcel
Brecher, Christian
INTERNATIONAL JOURNAL OF AUTOMATION TECHNOLOGY, 2022, 16 (03) : 261 - 268
[9] Real-time digital twin-based optimization with predictive simulation learning
Goodwin, Travis
Xu, Jie
Celik, Nurcin
Chen, Chun-Hung
JOURNAL OF SIMULATION, 2024, 18 (01) : 47 - 64
[10] Towards a digital twin framework in additive manufacturing: Machine learning and bayesian optimization for time series process optimization
Karkaria, Vispi
Goeckner, Anthony
Zha, Rujing
Chen, Jie
Zhang, Jianjing
Zhu, Qi
Cao, Jian
Gao, Robert X.
Chen, Wei
JOURNAL OF MANUFACTURING SYSTEMS, 2024, 75 : 322 - 332

← 1 2 3 4 5 →