AI-based reconfigurable inspection system (RIS): comprehensive model and implementation in industry

Simple item page
dc.contributor.authorKumar, A. Sarat
dc.contributor.authorMilisavljevic-Syed, Jelena
dc.date.accessioned2024-04-03T08:39:05Z
dc.date.available2024-04-03T08:39:05Z
dc.date.issued2024-01-12
dc.descriptionIn: 56th CIRP Conference on Manufacturing Systems, CIRP CMS ‘23, South Africaen_UK
dc.description.abstractDue to global competition and continuously changing customer demand, manufacturers nowadays face frequent and unpredictable market shifts. Introducing reconfigurability into contemporary manufacturing systems can enhance cost-effective and rapid responsiveness to these variations. A Reconfigurable Manufacturing System (RMS) can provide a tailored production process in response to changes in operating procedures or machine statuses. Just like any other manufacturing system, RMS requires effective and timely diagnosis as well as prognosis to function smoothly. A Reconfigurable Inspection System (RIS) is designed within an RMS for data-oriented detection of product quality with a minimum number of inspection units. Existing studies about reconfiguration, however, focus on production while disregarding inspection. Artificial Intelligence (AI) has the potential to significantly assist manufacturers over the next decade due to their heavy dependency on data. AI applications such as Machine learning (ML) and Deep Learning (DL) can aid in addressing issues such as tracking manufacturing failures back to specific phases in the manufacturing process by learning relevant data patterns. Thus, this paper aims to provide an overview of the current literature on RMS as well as ML/DL technologies that can be integrated into RIS to enhance performance. Subsequently, a comprehensive model of an AI-based RIS is proposed based on the experimental results derived from existing publications, and the retrofitting procedure of a case study is presented. However, the proposed model and the retrofitting procedure are not validated by experimental results or physical implementation.en_UK
dc.identifier.citationKumar AS, Milisavljevic-Syed J. (2023) AI-based reconfigurable inspection system (RIS): comprehensive model and implementation in industry. Procedia CIRP, Volume 120, 2023, pp.87-92en_UK
dc.identifier.eissn2212-8271
dc.identifier.urihttps://doi.org/10.1016/j.procir.2023.08.016
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/21119
dc.language.isoen_UKen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectArtificial Intelligenceen_UK
dc.subjectReconfigurable Manufacturing Systemen_UK
dc.subjectReconfigurable Inspection Systemen_UK
dc.subjectMachine Learningen_UK
dc.subjectDeep Learningen_UK
dc.titleAI-based reconfigurable inspection system (RIS): comprehensive model and implementation in industryen_UK
dc.typeConference paperen_UK
dcterms.dateAccepted2022-06-12
dcterms.dateAccepted2022-06-12

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
AI-based_reconfigurable_inspection_system-2023.pdf
Size:
680.92 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)