2020
Reichert, Lisa; Schmidt, Carsten; Horst, Peter
A Methodology for Strategic Individualization of Stiffened CFRP Structures Vortrag
Gulf of Naples, Italy, 15.07.2020.
Abstract | BibTeX | Schlagwörter: Composite Manufacturing, Design Method
@misc{Reichert2020,
title = {A Methodology for Strategic Individualization of Stiffened CFRP Structures},
author = {Lisa Reichert and Carsten Schmidt and Peter Horst},
editor = {CIRP ICME ’20 - 14th CIRP Conference on INTELLIGENT COMPUTATION IN MANUFACTURING ENGINEERING},
year = {2020},
date = {2020-07-15},
booktitle = {14th CIRP Conference on Intelligent Computation in Manufacturing Engineering},
address = {Gulf of Naples, Italy},
abstract = {The design process for stiffened lightweight structures faces many challenges. Current design goals, especially in the aircraft industry, demand significant decrease in structural weight while keeping development and manufacturing costs low. Therefore, more complex design choices are investigated and show promising potential. The resulting increase in computational effort presents a remaining challenge. A novel approach for the design of stiffened lightweight structures, that enables strategic application of individualization and therefore allocates computational effort to areas of a structure where high benefits can be expected, is presented. Starting from a structure with common parts, the concept of individualization, i.e. adapting the design to local loads, is applied iteratively to selected parts of the structure. The methodology of strategic individualization is explained in detail before being applied to an example structure. This application shows the general capability of the methodology to design lightweight structures with less computational effort compared to a conventional approach.},
keywords = {Composite Manufacturing, Design Method},
pubstate = {published},
tppubtype = {presentation}
}
The design process for stiffened lightweight structures faces many challenges. Current design goals, especially in the aircraft industry, demand significant decrease in structural weight while keeping development and manufacturing costs low. Therefore, more complex design choices are investigated and show promising potential. The resulting increase in computational effort presents a remaining challenge. A novel approach for the design of stiffened lightweight structures, that enables strategic application of individualization and therefore allocates computational effort to areas of a structure where high benefits can be expected, is presented. Starting from a structure with common parts, the concept of individualization, i.e. adapting the design to local loads, is applied iteratively to selected parts of the structure. The methodology of strategic individualization is explained in detail before being applied to an example structure. This application shows the general capability of the methodology to design lightweight structures with less computational effort compared to a conventional approach.
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.
2013
Denkena, Berend; Schmidt, Carsten; Hundt, Tobias; Köller, Marian
In: S. 855-859, 2013.
Abstract | Links | BibTeX | Schlagwörter: Composite Manufacturing, Manufacturing Quality, Tooling Costs
@article{Denkena2013b,
title = {Bewertung mehrstufiger CFK-Fertigungsprozesse: Analyse der Zusammenhänge zwischen Formwerkzeugkosten und Bauteilqualität bei der Herstellung von CFK-Bauteilen},
author = {Berend Denkena and Carsten Schmidt and Tobias Hundt and Marian Köller},
url = {Direct Link: http://www.zwf-online.de/ZW111034},
year = {2013},
date = {2013-11-01},
pages = {855-859},
abstract = {Die Bewertung von Bauteilqualität und Herstellkosten der Formwerkzeuge bei der Fertigung von CFK-Bauteilen ist ein wichtiger Faktor zur Auslegung von Prozessketten. Form- und Lageabweichungen von CFK-Bauteilen korrelieren mit den Material- und Oberflächeneigenschaften der Formwerkzeuge, die ihrerseits Einfluss auf die Kosten der Formwerkzeugfertigung haben. In diesem Beitrag wird der Zusammenhang zwischen den Kosten des Formwerkzeugs und der Bauteilqualität mit verschiedenen am IFW entwickelten Bewertungsansätzen für mehrstufige Fertigungsprozesse analysiert.},
keywords = {Composite Manufacturing, Manufacturing Quality, Tooling Costs},
pubstate = {published},
tppubtype = {article}
}
Die Bewertung von Bauteilqualität und Herstellkosten der Formwerkzeuge bei der Fertigung von CFK-Bauteilen ist ein wichtiger Faktor zur Auslegung von Prozessketten. Form- und Lageabweichungen von CFK-Bauteilen korrelieren mit den Material- und Oberflächeneigenschaften der Formwerkzeuge, die ihrerseits Einfluss auf die Kosten der Formwerkzeugfertigung haben. In diesem Beitrag wird der Zusammenhang zwischen den Kosten des Formwerkzeugs und der Bauteilqualität mit verschiedenen am IFW entwickelten Bewertungsansätzen für mehrstufige Fertigungsprozesse analysiert.