Details
| Original language | English |
|---|---|
| Article number | e202300165 |
| Number of pages | 7 |
| Journal | PAMM |
| Volume | 23 |
| Issue number | 4 |
| Publication status | Published - 23 Dec 2023 |
Abstract
ASJC Scopus subject areas
- Energy(all)
- Renewable Energy, Sustainability and the Environment
- Engineering(all)
- General Engineering
- Engineering(all)
- Computational Mechanics
Research Area (based on ÖFOS 2012)
- TECHNICAL SCIENCES
- Construction Engineering
- Civil Engineering
- Computational engineering
Sustainable Development Goals
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In: PAMM, Vol. 23, No. 4, e202300165, 23.12.2023.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Using conditional generative adversarial networks for the prediction of stresses in an adhesive composite
AU - Khan, Abdul Wasay
AU - Balzani, Claudio
N1 - This work was supported by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) in the “Add2ReliaBlade” project (Grant no. 0324335C). Open access funding enabled and organized by Projekt DEAL.
PY - 2023/12/23
Y1 - 2023/12/23
N2 - Fiber reinforced adhesives are an integral part of wind energy turbine blades and play an important role in evaluation of structural integrity of the blades. Digitization of manufacturing processes and demand for fast and efficient stress analysis argues for the application of deep learning. As a step forward in using deep learning to predict structural properties in composite, the work would explore the applicability of deep learning methods to 2D fiber reinforced composite. In this work, a composite adhesive used in wind energy turbine blades is used as an illustrative example. The composite adhesive when viewed using a CT scan machine shows a heterogeneous structure with two phases, a softer matrix phase and a much stiff fiber phase. For this purpose, several CT scan images of the fiber adhesive composite are used. The images on subsequent FEA analysis serves as the training data for the deep learning framework. Once trained, the neural network would be able to predict the stress distributions in the structure. We apply conditional adversarial networks as general purpose solutions to problems focused on stress prediction, which could be thought of as an image-to-image translation from the microstructure domain to the stress domain. These net-works in addition to learning the mapping from input image to output image also learn a loss function to train for this mapping. This makes the method more generic because otherwise different loss formulations for different problems have to be formulated.
AB - Fiber reinforced adhesives are an integral part of wind energy turbine blades and play an important role in evaluation of structural integrity of the blades. Digitization of manufacturing processes and demand for fast and efficient stress analysis argues for the application of deep learning. As a step forward in using deep learning to predict structural properties in composite, the work would explore the applicability of deep learning methods to 2D fiber reinforced composite. In this work, a composite adhesive used in wind energy turbine blades is used as an illustrative example. The composite adhesive when viewed using a CT scan machine shows a heterogeneous structure with two phases, a softer matrix phase and a much stiff fiber phase. For this purpose, several CT scan images of the fiber adhesive composite are used. The images on subsequent FEA analysis serves as the training data for the deep learning framework. Once trained, the neural network would be able to predict the stress distributions in the structure. We apply conditional adversarial networks as general purpose solutions to problems focused on stress prediction, which could be thought of as an image-to-image translation from the microstructure domain to the stress domain. These net-works in addition to learning the mapping from input image to output image also learn a loss function to train for this mapping. This makes the method more generic because otherwise different loss formulations for different problems have to be formulated.
U2 - 10.1002/pamm.202300165
DO - 10.1002/pamm.202300165
M3 - Article
VL - 23
JO - PAMM
JF - PAMM
SN - 1617-7061
IS - 4
M1 - e202300165
ER -