elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

ENHANCEMENTS OF AN INLINE QA SYSTEM FOR FIBER LAYUP PROCESSES

Meister, Sebastian and Kaestner, Sebastian and Krombholz, Christian (2018) ENHANCEMENTS OF AN INLINE QA SYSTEM FOR FIBER LAYUP PROCESSES. ISCM 2018, 21. - 22. Nov. 2018, Marknesse, The Netherlands.

[img] PDF
196kB

Abstract

The use of fibre-reinforced plastics for primary structures in current aircraft are increasing significantly due to the better specific properties compared to metallic designs. However, in order to be able to transfer the advantages from long range to short or medium range aircraft an increase in production efficiency and significant reduction of manufacturing costs is required. This paper therefore presents the errors encountered in fibre layup processes and a sensor system which enables automated detection of manufacturing deviations and drastically reduces times currently required for visual inspection. By detecting the geometric and optical properties of the laminate, potential defect areas are detected, classified, localized and measured in a cascading analysis. An exemplary setup that is directly mounted at an endeffector for fibre layup processes is shown in Figure 1. <FIGURE 1> The developed approach is highly adaptive for different and multiple sensor systems and multiple parallel and sequential working algorithms. Especially the detection and classification behaviour is analysed in this paper, with respect to various applied algorithms. For defect detection cascade, the method by Otsu and adaptive thresholding is considered. Evaluating the feature extraction step, Histogram of Oriented Gradients (HOG), RotationInvariant HOG (RIHOG), Local Binary Patterns (LBP), various texture based features and statistical moments are evaluated. For defect classification and defect class separation, a Support Vector Machine (SVM) is applied. An exemplarily detection and classification result is visualized in Figure 2. <FIGURE 2> The results show a significant increase in calculation speed and detection performance. The Method by Otsu performs better for poor quality or textured input data containing potentially more defects. Adaptive thresholding works best for high quality input data with less texture. The feature extraction operates best with all algorithms combined. Obviously, this increases calculation time and slow down the classification process. Therefore, this paper suggests an algorithm selection as a compromise between calculation speed and classification performance.

Item URL in elib:https://elib.dlr.de/122063/
Document Type:Conference or Workshop Item (Speech)
Title:ENHANCEMENTS OF AN INLINE QA SYSTEM FOR FIBER LAYUP PROCESSES
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Meister, SebastianSebastian.Meister (at) dlr.dehttps://orcid.org/0000-0002-8193-1143
Kaestner, SebastianDLR FA-VPT SDUNSPECIFIED
Krombholz, ChristianChristian.Krombholz (at) dlr.dehttps://orcid.org/0000-0002-1802-4110
Date:November 2018
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Accepted
Keywords:Quality Assurance, Computer Vision, Maschine Learning, Feature extraction, Automated Fibre Placement
Event Title:ISCM 2018
Event Location:Marknesse, The Netherlands
Event Type:international Conference
Event Dates:21. - 22. Nov. 2018
Organizer:NLR
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:fixed-wing aircraft
DLR - Research area:Aeronautics
DLR - Program:L AR - Aircraft Research
DLR - Research theme (Project):L - Structures and Materials
Location: Stade
Institutes and Institutions:Institute of Composite Structures and Adaptive Systems > Composite Process Technology
Deposited By: Meister, Sebastian
Deposited On:14 Dec 2018 12:00
Last Modified:31 Jul 2019 20:20

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.