Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/91421
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Industrial and Systems Engineering | - |
| dc.creator | Ma, X | - |
| dc.creator | Cheng, J | - |
| dc.creator | Qi, Q | - |
| dc.creator | Tao, F | - |
| dc.date.accessioned | 2021-11-03T06:53:32Z | - |
| dc.date.available | 2021-11-03T06:53:32Z | - |
| dc.identifier.issn | 2212-8271 | - |
| dc.identifier.uri | http://hdl.handle.net/10397/91421 | - |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier | en_US |
| dc.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) | en_US |
| dc.rights | 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 | en_US |
| dc.subject | Artificial intelligence (AI) | en_US |
| dc.subject | Digital twin shop-floor (DTS) | en_US |
| dc.subject | Real-time interaction | en_US |
| dc.title | Artificial intelligence enhanced interaction in digital twin shop-floor | en_US |
| dc.type | Journal/Magazine Article | en_US |
| dc.identifier.spage | 858 | - |
| dc.identifier.epage | 863 | - |
| dc.identifier.volume | 100 | - |
| dc.identifier.doi | 10.1016/j.procir.2021.05.031 | - |
| dcterms.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. | - |
| dcterms.accessRights | open access | en_US |
| dcterms.bibliographicCitation | Procedia CIRP, 2021, v. 100, p. 858-863 | - |
| dcterms.isPartOf | Procedia CIRP | - |
| dcterms.issued | 2021 | - |
| dc.identifier.scopus | 2-s2.0-85107885361 | - |
| dc.description.validate | 202110 bcvc | - |
| dc.description.oa | Version of Record | en_US |
| dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
| dc.description.pubStatus | Published | en_US |
| Appears in Collections: | Journal/Magazine Article | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 1-s2.0-S2212827121004935-main.pdf | 795.72 kB | Adobe PDF | View/Open |
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