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Learning defects from aircraft NDT data

Prakash, Navya and Nieberl, Dorothea and Mayer, Monika and Schuster, Alfons (2023) Learning defects from aircraft NDT data. NDT and E International, 138. Elsevier. doi: 10.1016/j.ndteint.2023.102885. ISSN 0963-8695.

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Official URL: https://www.sciencedirect.com/science/article/pii/S0963869523001007?utm_campaign=STMJ_AUTH_SERV_PUBLISHED&utm_medium=email&utm_acid=113998212&SIS_ID=&dgcid=STMJ_AUTH_SERV_PUBLISHED&CMX_ID=&utm_in=DM376211&utm_source=AC_


Non-destructive evaluation of aircraft production is optimised and digitalised with Industry 4.0. The aircraft structures produced using fibre metal laminate are traditionally inspected using water-coupled ultrasound scans and manually evaluated. This article proposes Machine Learning models to examine the defects in ultrasonic scans of A380 aircraft components. The proposed approach includes embedded image feature extraction methods and classifiers to learn defects in the scan images. The proposed algorithm is evaluated by benchmarking embedded classifiers and further promoted to research with an industry-based certification process. The HoG-Linear SVM classifier has outperformed SURF-Decision Fine Tree in detecting potential defects. The certification process uses the Probability of Detection function, substantiating that the HoG-Linear SVM classifier detects minor defects. The experimental trials prove that the proposed method will be helpful to examiners in the quality control and assurance of aircraft production, thus leading to significant contributions to non-destructive evaluation 4.0.

Item URL in elib:https://elib.dlr.de/195514/
Document Type:Article
Title:Learning defects from aircraft NDT data
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Prakash, NavyaUNSPECIFIEDhttps://orcid.org/0000-0002-7466-4470UNSPECIFIED
Nieberl, DorotheaUNSPECIFIEDhttps://orcid.org/0000-0001-7546-5476UNSPECIFIED
Mayer, MonikaUNSPECIFIEDhttps://orcid.org/0000-0002-4448-9501UNSPECIFIED
Schuster, AlfonsUNSPECIFIEDhttps://orcid.org/0000-0002-7444-366XUNSPECIFIED
Date:2 June 2023
Journal or Publication Title:NDT and E International
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Keywords:NDT NDE 4.0 Aircraft production Quality control Machine learning POD
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Components and Systems
DLR - Research area:Aeronautics
DLR - Program:L CS - Components and Systems
DLR - Research theme (Project):L - Production Technologies
Location: Augsburg
Institutes and Institutions:Institute of Structures and Design > Automation and Production Technology
Deposited By: Schuster, Dr.-Ing. Alfons
Deposited On:16 Jun 2023 10:26
Last Modified:16 Jun 2023 10:26

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