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Review

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

by 1,*,†, 2,*,†, 3, 1 and 1
1
Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, D-52074 Aachen, Germany
2
UBTECH North America Research and Development Center, Pasadena, CA 91101-4858, USA
3
Department of Statistics, University of California Los Angeles, Los Angeles, CA 90095-1554, USA
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2021, 21(19), 6340; https://doi.org/10.3390/s21196340
Received: 15 July 2021 / Revised: 9 September 2021 / Accepted: 10 September 2021 / Published: 23 September 2021
(This article belongs to the Special Issue Digital Twins, Sensing Technologies and Automation in Industry 4.0)
Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined. View Full-Text
Keywords: artificial intelligence; machine learning; deep learning; digital twin; digital shadow; Industry 4.0; sustainability; sustainable smart manufacturing; robotics; review artificial intelligence; machine learning; deep learning; digital twin; digital shadow; Industry 4.0; sustainability; sustainable smart manufacturing; robotics; review
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MDPI and ACS Style

Huang, Z.; Shen, Y.; Li, J.; Fey, M.; Brecher, C. A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics. Sensors 2021, 21, 6340. https://doi.org/10.3390/s21196340

AMA Style

Huang Z, Shen Y, Li J, Fey M, Brecher C. A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics. Sensors. 2021; 21(19):6340. https://doi.org/10.3390/s21196340

Chicago/Turabian Style

Huang, Ziqi, Yang Shen, Jiayi Li, Marcel Fey, and Christian Brecher. 2021. "A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics" Sensors 21, no. 19: 6340. https://doi.org/10.3390/s21196340

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