Performance evaluation of CNN and R‑CNN based line by line analysis algorithms for fibre placement defect classification

Meister, Sebastian and Wermes, Mahdieu A. M. (2022) Performance evaluation of CNN and R‑CNN based line by line analysis algorithms for fibre placement defect classification. Production Engineering. Springer. doi: 10.1007/s11740-022-01162-7. ISSN 0944-6524.

PDF - Published version
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Item URL in elib:

https://elib.dlr.de/188673/

Document Type:

Article

Title:

Performance evaluation of CNN and R‑CNN based line by line analysis algorithms for fibre placement defect classification

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Meister, Sebastian	Sebastian.Meister (at) dlr.de	https://orcid.org/0000-0002-8193-1143	UNSPECIFIED
Wermes, Mahdieu A. M.	Mahdieu.Wermes (at) dlr.de	UNSPECIFIED	UNSPECIFIED

Date:

30 September 2022

Journal or Publication Title:

Production Engineering

Refereed publication:

Yes

Open Access:

Yes

Gold Open Access:

In SCOPUS:

Yes

In ISI Web of Science:

Yes

DOI:

10.1007/s11740-022-01162-7

Publisher:

Springer

ISSN:

0944-6524

Status:

Published

Keywords:

Recurrent neural networks, Automated fibre placement, Inline inspection, Generative adversarial networks, Laser line scan sensor

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:

Stade

Institutes and Institutions:

Institute of Composite Structures and Adaptive Systems > Composite Process Technology

Deposited By:

Meister, Dr. Sebastian

Deposited On:

17 Oct 2022 11:51

Last Modified:

28 Jun 2023 13:03

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