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http://hdl.handle.net/10397/91421
| Title: | Artificial intelligence enhanced interaction in digital twin shop-floor | Authors: | Ma, X Cheng, J Qi, Q Tao, F |
Issue Date: | 2021 | Source: | Procedia CIRP, 2021, v. 100, p. 858-863 | Abstract: | As an enabling technology for smart manufacturing, digital twin has been widely applied in manufacturing shop-floor. A great deal of research focuses on the key issues in implementing digital twin shop-floor (DTS), including scheduling, production planning, fault diagnosis and prognostics. However, DTS puts forward higher requirements in terms of real-time interaction. Artificial intelligence (AI), as an effective approach to improve the intelligence of the physical shop-floor, provides a new method to meet the above requirements. In this paper, a framework of AI-enhanced DTS in interaction is proposed. AI-enhanced DTS improves the real-time interaction through predictive control. The implementation mechanism of AI-enhanced interaction in DTS is also presented in detail. Enabling technologies for interaction in DTS are introduced at last. | Keywords: | Artificial intelligence (AI) Digital twin shop-floor (DTS) Real-time interaction |
Publisher: | Elsevier | Journal: | Procedia CIRP | ISSN: | 2212-8271 | DOI: | 10.1016/j.procir.2021.05.031 | Rights: | © 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0) The following publication Ma, X., Cheng, J., Qi, Q., & Tao, F. (2021). Artificial intelligence enhanced interaction in digital twin shop-floor. Procedia CIRP, 100, 858-863 is available at https://doi.org/10.1016/j.procir.2021.05.031 |
| Appears in Collections: | Journal/Magazine Article |
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| 1-s2.0-S2212827121004935-main.pdf | 795.72 kB | Adobe PDF | View/Open |
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