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Abstract Aerospace composites assemblies/joining demand ultra-high precision due to critical safety requirements, which necessitate adherence to indicators of risk that are often difficult to quantify. This study examines one important indicator, the residual stress that arises as a result of dimensional mismatch between mating components during the composite structures assembly process. Conventional simulations of large components assemblies investigated the process at a local or global scale, but lacked detailed exploitation of multi-layer stress analysis at integrated scale for composite structures. We develop a novel digital twin simulation for joining large composite structures with mechanical fasteners. The digital twin simulation integrates global features and local features for detailed investigation of stresses. We perform a statistical analysis to better understand the numerical properties of residual stresses after the fastening. Goodness-of-Fit tests and normality tests are used to explore the probabilistic distributions of the stresses exceeding a chosen safety threshold. The case study is conducted based on composite fuselage joining. The results show the stresses in composite structures assembly follow extreme value distributions (such as Weibull, Gumbel) rather than the widely used Gaussian distribution. The stresses in joined composite structures differ across layers, which can be attributed to the anisotropic material behavior.
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Towards data-efficient mechanical design of bicontinuous composites using generative AI
The distribution of material phases is crucial to determine the composite's mechanical property. While the full structure-mechanics relationship of highly ordered material distributions can be studied with finite number of cases, this relationship is difficult to be revealed for complex irregular distributions, preventing design of such material structures to meet certain mechanical requirements. The noticeable developments of artificial intelligence (AI) algorithms in material design enables to detect the hidden structure-mechanics correlations which is essential for designing composite of complex structures. It is intriguing how these tools can assist composite design. Here, we focus on the rapid generation of bicontinuous composite structures together with the stress distribution in loading. We find that generative AI, enabled through fine-tuned Low Rank Adaptation models, can be trained with a few inputs to generate both synthetic composite structures and the corresponding von Mises stress distribution. The results show that this technique is convenient in generating massive composites designs with useful mechanical information that dictate stiffness, fracture and robustness of the material with one model, and such has to be done by several different experimental or simulation tests. This research offers valuable insights for the improvement of composite design with the goal of expanding the design space and automatic screening of composite designs for improved mechanical functions.
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- Award ID(s):
- 2145392
- PAR ID:
- 10507014
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Theoretical and Applied Mechanics Letters
- Volume:
- 14
- Issue:
- 1
- ISSN:
- 2095-0349
- Page Range / eLocation ID:
- 100492
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.taml.2024.100492
