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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512418/ https://www.ncbi.nlm.nih.gov/pubmed/34640660 http://dx.doi.org/10.3390/s21196340 |
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author | Huang, Ziqi Shen, Yang Li, Jiayi Fey, Marcel Brecher, Christian |
author_facet | Huang, Ziqi Shen, Yang Li, Jiayi Fey, Marcel Brecher, Christian |
author_sort | Huang, Ziqi |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-8512418 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85124182021-10-14 A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics Huang, Ziqi Shen, Yang Li, Jiayi Fey, Marcel Brecher, Christian Sensors (Basel) Review 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. MDPI 2021-09-23 /pmc/articles/PMC8512418/ /pubmed/34640660 http://dx.doi.org/10.3390/s21196340 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Huang, Ziqi Shen, Yang Li, Jiayi Fey, Marcel Brecher, Christian A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics |
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 | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512418/ https://www.ncbi.nlm.nih.gov/pubmed/34640660 http://dx.doi.org/10.3390/s21196340 |
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