Manufacturing quality assessment in the industry 4.0 era: a review
File(s)
Author(s)
Markatos, Nikolaos G
Mousavi, Alireza
Type
Journal Article
Abstract
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.
Date Issued
2023
Date Acceptance
2023-03-01
Citation
Total Quality Management and Business Excellence, 2023, 34 (13-14), pp.1655-1681
ISSN
1360-0613
Publisher
Taylor and Francis Group
Start Page
1655
End Page
1681
Journal / Book Title
Total Quality Management and Business Excellence
Volume
34
Issue
13-14
Copyright Statement
© 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 (http://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.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://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.
License URL
Identifier
http://dx.doi.org/10.1080/14783363.2023.2194524
Publication Status
Published
Date Publish Online
2023-03-29