Research on topology optimization algorithm in additive manufacturing of continuous fiber reinforced composites based on deep learning Article Swipe
Jochen Lang
,
Yaohui Zhao
,
Shuquan Xu
,
Huan Yang
,
Tao Bai
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/3019/1/012055
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/3019/1/012055
With the rapid development of additive manufacturing technology, continuous fiber reinforced composites (CFRCs) have gradually become the preferred material for high-performance structures due to their excellent mechanical properties. However, in the additive manufacturing process, how to reasonably optimize the fiber distribution of composite materials to improve structural performance and material utilization remains a difficult problem. Although traditional topology optimization methods can optimize the distribution of materials, it is difficult to fully consider the nonlinear characteristics of the fiber direction in complex composite structures. To this end, this paper proposes a hybrid topology optimization algorithm based on deep learning, combining CNN with traditional topology optimization algorithms to optimize the design of fiber reinforced composites in additive manufacturing. The algorithm automatically learns and predicts the optimal fiber layout through deep learning model training, and combines it with traditional topology optimization algorithms to form an efficient optimization framework. Through a large amount of experimental data and simulation verification, this paper demonstrates the optimization effect of this method under different loading conditions. Compared with the conventional method, the proposed method can greatly increase the rigidity and strength of the composite structure and reduce the waste of materials. The optimal efficiency is about 30%. Moreover, the computation time of this method is reduced by 20%, which shows that it has great potential in additive manufacturing.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/3019/1/012055
- OA Status
- diamond
- References
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411062002
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411062002Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1742-6596/3019/1/012055Digital Object Identifier
- Title
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Research on topology optimization algorithm in additive manufacturing of continuous fiber reinforced composites based on deep learningWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-05-01Full publication date if available
- Authors
-
Jochen Lang, Yaohui Zhao, Shuquan Xu, Huan Yang, Tao BaiList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/3019/1/012055Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1088/1742-6596/3019/1/012055Direct OA link when available
- Concepts
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Composite material, Topology optimization, Fiber, Materials science, Algorithm, Topology (electrical circuits), Computer science, Mathematics, Structural engineering, Engineering, Finite element method, CombinatoricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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5Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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