Using machine learning to increase efficiency in design of experiments for cyclic characterization of fibre-reinforced plastics
Gadinger M, Witzgall C, Hufnagel T, Wartzack S (2023)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2023
Publisher: The Design Society
Pages Range: 115–124
Conference Proceedings Title: Proceedings of the 34th Symposium Design for X (DFX2023)
Event location: Radebeul
DOI: 10.35199/dfx2023.12
Abstract
Efficient characterization of fatigue behavior plays a crucial role in engineering design as it reduces the financial costs associated with expensive experimental tests. Existing methods for characterizing the fatigue behavior of fibre-reinforced plastics have proven inefficient due to the oversight of important design parameters, such as fibre orientations. To address this challenge, we propose an innovative approach based on Gaussian process regression. Our approach integrates previously unaccounted design parameters into the decision-making process, ensuring that optimal design points are selected for testing. By doing so, we maximize the gain of knowledge within the model, resulting in improved efficiency and accurate characterization of fatigue behavior.
Authors with CRIS profile
Marc Gadinger
Chair of Engineering Design
Christian Witzgall
Chair of Engineering Design
Thomas Hufnagel
Chair of Engineering Design
Sandro Wartzack
Chair of Engineering Design
How to cite
APA:
Gadinger, M., Witzgall, C., Hufnagel, T., & Wartzack, S. (2023). Using machine learning to increase efficiency in design of experiments for cyclic characterization of fibre-reinforced plastics. In Dieter Krause, Kristin Paetzold-Byhain, Sandro Wartzack (Eds.), Proceedings of the 34th Symposium Design for X (DFX2023) (pp. 115–124). Radebeul, DE: The Design Society.
MLA:
Gadinger, Marc, et al. "Using machine learning to increase efficiency in design of experiments for cyclic characterization of fibre-reinforced plastics." Proceedings of the 34th Symposium Design for X (DFX2023), Radebeul Ed. Dieter Krause, Kristin Paetzold-Byhain, Sandro Wartzack, The Design Society, 2023. 115–124.
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