A Machine Learning Based Model for Monitoring of Composites' Drilling-Induced Defects During Assembly Production Using Terahertz Imaging Data

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/26904

Title:	A Machine Learning Based Model for Monitoring of Composites' Drilling-Induced Defects During Assembly Production Using Terahertz Imaging Data
Authors:	Amini, A Gan, TH
Keywords:	terahertz;composites;drilling;machine learning;convolutional neural networks;image processing;signal processing
Issue Date:	6-Oct-2022
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
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.
URI:	https://bura.brunel.ac.uk/handle/2438/26904
DOI:	https://doi.org/10.1109/COMPENG50184.2022.9905438
ISBN:	978-1-7281-7124-1 (ebk) 978-1-7281-7125-8 (PoD)
ISSN:	2688-2566
Other Identifiers:	ORCID iDs: Amin Amini https://orcid.org/0000-0001-7081-2440; Tat Hean Gan https://orcid.org/0000-0002-5598-8453.
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