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http://bura.brunel.ac.uk/handle/2438/26904Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Amini, A | - |
| dc.contributor.author | Gan, TH | - |
| dc.coverage.spatial | Florence, Italy | - |
| dc.date.accessioned | 2023-08-06T16:58:38Z | - |
| dc.date.available | 2023-08-06T16:58:38Z | - |
| dc.date.issued | 2022-10-06 | - |
| dc.identifier | ORCID iDs: Amin Amini https://orcid.org/0000-0001-7081-2440; Tat Hean Gan https://orcid.org/0000-0002-5598-8453. | - |
| dc.identifier.citation | Amini, A. and Gan, T.H. (2022) 'A Machine Learning Based Model for Monitoring of Composites' Drilling-Induced Defects During Assembly Production Using Terahertz Imaging Data', 2022 IEEE Workshop on Complexity in Engineering, COMPENG 2022, Florence, Italy, 18-20 July, pp. 1 - 5. doi: 10.1109/COMPENG50184.2022.9905438. | en_US |
| dc.identifier.isbn | 978-1-7281-7124-1 (ebk) | - |
| dc.identifier.isbn | 978-1-7281-7125-8 (PoD) | - |
| dc.identifier.issn | 2688-2566 | - |
| dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/26904 | - |
| dc.description.sponsorship | The work presented in this paper is part of the collaborative research project, Automated Terahertz Imaging of Composites and tooling profiling (ATTIC) funded by the Collaborative R&D: Photonics for Advanced Manufacturing under grant agreement number 106162. | en_US |
| dc.format.extent | 1 - 5 | - |
| dc.format.medium | Print-Electronic | - |
| dc.language | English | - |
| dc.language.iso | en_US | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
| dc.rights | Copyright © 2022 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. For more information, see https://www.ieee.org/publications/rights/rights-policies.html | - |
| dc.rights.uri | https://www.ieee.org/publications/rights/rights-policies.html | - |
| dc.source | 2022 IEEE Workshop on Complexity in Engineering (COMPENG) | - |
| dc.source | 2022 IEEE Workshop on Complexity in Engineering (COMPENG) | - |
| dc.subject | terahertz | en_US |
| dc.subject | composites | en_US |
| dc.subject | drilling | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | convolutional neural networks | en_US |
| dc.subject | image processing | en_US |
| dc.subject | signal processing | en_US |
| dc.title | A Machine Learning Based Model for Monitoring of Composites' Drilling-Induced Defects During Assembly Production Using Terahertz Imaging Data | en_US |
| dc.type | Conference Paper | en_US |
| dc.identifier.doi | https://doi.org/10.1109/COMPENG50184.2022.9905438 | - |
| dc.relation.isPartOf | 2022 IEEE Workshop on Complexity in Engineering, COMPENG 2022 | - |
| pubs.finish-date | 2022-07-20 | - |
| pubs.finish-date | 2022-07-20 | - |
| pubs.publication-status | Published | - |
| pubs.start-date | 2022-07-18 | - |
| pubs.start-date | 2022-07-18 | - |
| dc.identifier.eissn | 2688-2582 | - |
| dc.rights.holder | Institute of Electrical and Electronics Engineers (IEEE) | - |
| Appears in Collections: | Brunel Innovation Centre | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | Copyright © 2022 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. For more information, see https://www.ieee.org/publications/rights/rights-policies.html | 3.93 MB | Adobe PDF | View/Open |
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