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.
2017
Schmidt, Carsten; Denkena, Berend; Hocke, Tristan; Völtzer, Klaas
1st CIRP Conference on Composite Materials Parts Manufacturing, 2017.
Links | BibTeX | Schlagwörter: Automated Fiber Placement, Process Monitoring, Quality Assurance, Thermal Imaging
@conference{Schmidt2017b,
title = {Influence of AFP process parameters on the temperature distribution used for thermal in-process monitoring},
author = {Carsten Schmidt and Berend Denkena and Tristan Hocke and Klaas Völtzer},
editor = {Procedia CIRP 66},
doi = {10.1016/j.procir.2017.03.220},
year = {2017},
date = {2017-06-07},
booktitle = {1st CIRP Conference on Composite Materials Parts Manufacturing},
pages = {68-73},
keywords = {Automated Fiber Placement, Process Monitoring, Quality Assurance, Thermal Imaging},
pubstate = {published},
tppubtype = {conference}
}
2014
Schmidt, Carsten; Schultz, Cedric; Weber, Patricc; Denkena, Berend
In: Composites PArt B: Engineering, Bd. 56, S. 109-116, 2014.
Abstract | Links | BibTeX | Schlagwörter: Automated Fiber Placement, Composite Structures, Eddy Current Testing, Process Monitoring, Quality Assurance
@article{Schmidt2014,
title = {Evaluation of eddy current testing for quality assurance and process monitoring of automated fiber placement},
author = {Carsten Schmidt and Cedric Schultz and Patricc Weber and Berend Denkena},
url = {http://dx.doi.org/10.1016/j.compositesb.2013.08.061},
year = {2014},
date = {2014-01-01},
journal = {Composites PArt B: Engineering},
volume = {56},
pages = {109-116},
abstract = {Standards in energy and cost efficiency are higher the ever especially in the aerospace industry. While structures made from carbon-fiber reinforced plastics (CFRP) show significant advantages in regards to specific strength and lightweight design, further improvements in their production processes are essential in order for CFRP to be competitive in the future. The authors present eddy current (EC) testing as a means for quality assurance (QA) and process monitoring for CFRP parts produced by automatic fiber placement (AFP), which is one the most prevalent production methods in aerospace industry. Eddy current testing shows the potential for highly automated process monitoring that can reduce error correction and cycle time in AFP.},
keywords = {Automated Fiber Placement, Composite Structures, Eddy Current Testing, Process Monitoring, Quality Assurance},
pubstate = {published},
tppubtype = {article}
}
Standards in energy and cost efficiency are higher the ever especially in the aerospace industry. While structures made from carbon-fiber reinforced plastics (CFRP) show significant advantages in regards to specific strength and lightweight design, further improvements in their production processes are essential in order for CFRP to be competitive in the future. The authors present eddy current (EC) testing as a means for quality assurance (QA) and process monitoring for CFRP parts produced by automatic fiber placement (AFP), which is one the most prevalent production methods in aerospace industry. Eddy current testing shows the potential for highly automated process monitoring that can reduce error correction and cycle time in AFP.