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DC Field | Value | Language |
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dc.contributor.author | Markatos, NG | - |
dc.contributor.author | Mousavi, A | - |
dc.date.accessioned | 2023-04-22T10:43:30Z | - |
dc.date.available | 2023-04-22T10:43:30Z | - |
dc.date.issued | 2023-03-29 | - |
dc.identifier | ORCID iDs: Nikolaos Markatos https://orcid.org/0000-0003-3953-6796; Alireza Mousavi https://orcid.org/0000-0003-0360-2712. | - |
dc.identifier.citation | Markatos, N.G. and Mousavi, A. (2023) 'Manufacturing quality assessment in the industry 4.0 era: a review', Total Quality Management & Business Excellence, 0 (ahead of print), pp. 1 - 27. doi: 10.1080/14783363.2023.2194524. | en_US |
dc.identifier.issn | 1478-3363 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/26299 | - |
dc.description.abstract | Copyright © 2023 The Author(s). Maintaining high-quality standards has consistently been the main goal of industries. With rising demand and customisation, industries must strike a balance between cost, manufacturing time, and quality. The technological advancements of Industry 4.0 have allowed the implementation of accurate quality prediction frameworks in the manufacturing lines. For quality prediction in manufacturing, machine learning, and artificial intelligence offer several benefits, but there are also a number of limitations that must be taken into consideration. The current study aims to highlight the aforementioned benefits and drawbacks. To do this, a literature review on the area of quality prediction and monitoring in Industry 4.0 manufacturing lines is conducted. The results demonstrate that the merits of the reviewed methods are many but six significant drawbacks must be accounted for the successful implementation of the studied quality prediction frameworks. The current study can serve as a ‘map’ for production managers in industries as well as experts in the field of manufacturing as they weigh the benefits and drawbacks of popular quality prediction models, as it provides information needed to determine to what extent these methods can be applied to new or existing manufacturing lines. | en_US |
dc.description.sponsorship | European Union’s Horizon 2020 Framework Programme research and innovation programme under grant agreement No. 820677[Q1] IQONIC project. | en_US |
dc.format.extent | 1 - 27 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en | en_US |
dc.publisher | Routledge (Taylor & Francis Group) | en_US |
dc.rights | https://creativecommons.org/licenses/by/4.0/ | - |
dc.rights | Copyright © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. | - |
dc.subject | evolution of quality | en_US |
dc.subject | quality prediction | en_US |
dc.subject | manufacturing | en_US |
dc.subject | Industry 4.0 | en_US |
dc.title | Manufacturing quality assessment in the industry 4.0 era: a review | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1080/14783363.2023.2194524 | - |
dc.relation.isPartOf | Total Quality Management & Business Excellence | - |
pubs.publication-status | Published online | - |
dc.identifier.eissn | 1478-3371 | - |
dc.rights.holder | The Author(s) | - |
Appears in Collections: | Dept of Civil and Environmental Engineering Research Papers |
Files in This Item:
File | Description | Size | Format | |
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FullText.pdf | Copyright © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. | 1.07 MB | Adobe PDF | View/Open |
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