2019
Schmidt, Carsten; Hocke, Tristan; Denkena, Berend
Artificial intelligence for non-destructive testing of CFRP prepreg materials Artikel
In: Production Engineering, S. 1-10, 2019.
Abstract | Links | BibTeX | Schlagwörter: Artificial Intelligence, Automated Fiber Placement, Composite Manufacturing, Composite Structures, Defects, Prepreg, Quality Assurance, Thermal Imaging
@article{Schmidt2019,
title = {Artificial intelligence for non-destructive testing of CFRP prepreg materials},
author = {Carsten Schmidt and Tristan Hocke and Berend Denkena},
url = {https://link.springer.com/article/10.1007%2Fs11740-019-00913-3},
doi = {https://doi.org/10.1007/s11740-019-00913-3},
year = {2019},
date = {2019-07-02},
journal = {Production Engineering},
pages = {1-10},
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, Composite Manufacturing, Composite Structures, Defects, Prepreg, Quality Assurance, Thermal Imaging},
pubstate = {published},
tppubtype = {article}
}
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.
Hocke, Tristan
Künstliche Intelligenz in der Fertigungsüberwachung von CFK-Bauteilen im Flugzeugbau Vortrag
12.02.2019.
BibTeX | Schlagwörter: Artificial Intelligence, Automated Fiber Placement, Process Monitoring
@misc{Hocke2019,
title = {Künstliche Intelligenz in der Fertigungsüberwachung von CFK-Bauteilen im Flugzeugbau},
author = {Tristan Hocke},
editor = {Künstliche Intelligenz in der Automatisierungstechnik - Automatisierungsforum Westküste},
year = {2019},
date = {2019-02-12},
keywords = {Artificial Intelligence, Automated Fiber Placement, Process Monitoring},
pubstate = {published},
tppubtype = {presentation}
}