A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics
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 algorith...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/19/6340 |
| _version_ | 1797515751412727808 |
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| author | Ziqi Huang Yang Shen Jiayi Li Marcel Fey Christian Brecher |
| author_facet | Ziqi Huang Yang Shen Jiayi Li Marcel Fey Christian Brecher |
| author_sort | Ziqi Huang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:51:42Z |
| format | Article |
| id | doaj.art-03351cfccf004e93a2fa47c180977531 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T06:51:42Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-03351cfccf004e93a2fa47c1809775312023-11-22T16:44:35ZengMDPI AGSensors1424-82202021-09-012119634010.3390/s21196340A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced RoboticsZiqi Huang0Yang Shen1Jiayi Li2Marcel Fey3Christian Brecher4Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, D-52074 Aachen, GermanyUBTECH North America Research and Development Center, Pasadena, CA 91101-4858, USADepartment of Statistics, University of California Los Angeles, Los Angeles, CA 90095-1554, USALaboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, D-52074 Aachen, GermanyLaboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, D-52074 Aachen, GermanyDigital 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.https://www.mdpi.com/1424-8220/21/19/6340artificial intelligencemachine learningdeep learningdigital twindigital shadowIndustry 4.0 |
| spellingShingle | Ziqi Huang Yang Shen Jiayi Li Marcel Fey Christian Brecher A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics Sensors artificial intelligence machine learning deep learning digital twin digital shadow Industry 4.0 |
| title | A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics |
| title_full | A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics |
| title_fullStr | A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics |
| title_full_unstemmed | A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics |
| title_short | A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics |
| title_sort | survey on ai driven digital twins in industry 4 0 smart manufacturing and advanced robotics |
| topic | artificial intelligence machine learning deep learning digital twin digital shadow Industry 4.0 |
| url | https://www.mdpi.com/1424-8220/21/19/6340 |
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