Details
| Original language | English |
|---|---|
| Pages (from-to) | 617-626 |
| Number of pages | 10 |
| Journal | Organic Process Research and Development |
| Volume | 13 |
| Issue number | 5 |
| Early online date | 6 Jul 2019 |
| Publication status | Published - Oct 2019 |
Abstract
This paper presents a concept of the quality assurance for CFRP prepreg materials and focusses on the classification of thermographic images using convolution neural networks (CNNs). The method for non-destructive testing of CFRP prepreg materials combines a laser-triangulation sensor and an infrared camera to monitor both, the geometry and the impregnation of the prepreg material. The aim is to ensure a high material quality excluding any defective material in an early stage of the process chain of the production of CFRP components. As a result, the reliability of Automated-Fiber-Placement processes utilizing this previously tested material increases. Therefore, an artificial intelligence is set up to classify the thermal images of the CFRP material. Two different architectures of CNN are trained and validated with data sets consisting of thermal images of several prepreg materials and different material defects, such as geometric deviations and varying fiber-matrix-ratios caused by an incorrect impregnation. The CNNs are able to differentiate prepreg materials and to detect and classify certain material-independent defects for known and trained prepreg materials.
Keywords
- Artificial Intelligence, Automated-Fiber-Placement, Defects, Prepreg, Quality assurance
ASJC Scopus subject areas
- Engineering(all)
- Mechanical Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Organic Process Research and Development, Vol. 13, No. 5, 10.2019, p. 617-626.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Artificial intelligence for non-destructive testing of CFRP prepreg materials
AU - Schmidt, Carsten
AU - Hocke, Tristan
AU - Denkena, Berend
N1 - Funding information: The authors would like to thank the Federal state of Lower Saxony and the Volkswagen Foundation for funding the research project “Multi-Matrix-Prepreg”. For further information, visit the website www.hpcfk.de (Grand No. ZN3063).
PY - 2019/10
Y1 - 2019/10
N2 - This paper presents a concept of the quality assurance for CFRP prepreg materials and focusses on the classification of thermographic images using convolution neural networks (CNNs). The method for non-destructive testing of CFRP prepreg materials combines a laser-triangulation sensor and an infrared camera to monitor both, the geometry and the impregnation of the prepreg material. The aim is to ensure a high material quality excluding any defective material in an early stage of the process chain of the production of CFRP components. As a result, the reliability of Automated-Fiber-Placement processes utilizing this previously tested material increases. Therefore, an artificial intelligence is set up to classify the thermal images of the CFRP material. Two different architectures of CNN are trained and validated with data sets consisting of thermal images of several prepreg materials and different material defects, such as geometric deviations and varying fiber-matrix-ratios caused by an incorrect impregnation. The CNNs are able to differentiate prepreg materials and to detect and classify certain material-independent defects for known and trained prepreg materials.
AB - This paper presents a concept of the quality assurance for CFRP prepreg materials and focusses on the classification of thermographic images using convolution neural networks (CNNs). The method for non-destructive testing of CFRP prepreg materials combines a laser-triangulation sensor and an infrared camera to monitor both, the geometry and the impregnation of the prepreg material. The aim is to ensure a high material quality excluding any defective material in an early stage of the process chain of the production of CFRP components. As a result, the reliability of Automated-Fiber-Placement processes utilizing this previously tested material increases. Therefore, an artificial intelligence is set up to classify the thermal images of the CFRP material. Two different architectures of CNN are trained and validated with data sets consisting of thermal images of several prepreg materials and different material defects, such as geometric deviations and varying fiber-matrix-ratios caused by an incorrect impregnation. The CNNs are able to differentiate prepreg materials and to detect and classify certain material-independent defects for known and trained prepreg materials.
KW - Artificial Intelligence
KW - Automated-Fiber-Placement
KW - Defects
KW - Prepreg
KW - Quality assurance
UR - http://www.scopus.com/inward/record.url?scp=85068864229&partnerID=8YFLogxK
U2 - 10.1007/s11740-019-00913-3
DO - 10.1007/s11740-019-00913-3
M3 - Article
AN - SCOPUS:85068864229
VL - 13
SP - 617
EP - 626
JO - Organic Process Research and Development
JF - Organic Process Research and Development
SN - 1083-6160
IS - 5
ER -