Machine learning-assisted finite element modeling of additively manufactured meta-materials Article Swipe
Alexander Meynen
,
H.M.A. Kolken
,
Michiel Mulier
,
Amir A. Zadpoor
,
Lennart Scheys
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s41205-025-00286-7
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s41205-025-00286-7
Mechanical characterization of three-dimensional (3D) printed meta-biomaterials is rapidly becoming a crucial step in the development of novel medical device concepts, including those used in functionally graded implants for orthopedic applications. Finite element simulations are a valid, FDA-acknowledged alternative to experimental tests, which are time-consuming, expensive, and labor-intensive. However, when applied to 3D-printed meta-biomaterials, state-of-the-art finite element modeling approaches are becoming increasingly complex, while their accuracy remains limited. A critical condition for accurate simulation results is the identification of correct modelling parameters. This study proposes a machine learning-based strategy for identifying model parameters, including material properties and model boundary conditions, to enable accurate simulations of macro-scale mechanical behavior. To achieve this goal, a physics-informed artificial neural network model (PIANN) was developed and trained using data generated through a fully automated finite element modeling workflow. Subsequently, the PIANN model was then tested using real experimental force-displacement data as its input. The experimental data from 3D-printed structures were used to predict the associated parameters for finite element modeling. Finally, the workflow was validated by qualitatively and quantitatively comparing simulation results to the experimental data. Based on these results, we concluded that the proposed workflow could identify model parameters such that the predictions of associated finite element simulations are in agreement with experimental observations. Furthermore, resulting finite element models were found to outperform state-of-the-art models in terms of both quantitative and qualitative accuracy. Therefore, the proposed strategy has the potential to facilitate the broader application of finite element simulations in evaluating 3D-printed parts, in general, and 3D-printed meta-biomaterials, in particular.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s41205-025-00286-7
- https://threedmedprint.biomedcentral.com/counter/pdf/10.1186/s41205-025-00286-7
- OA Status
- gold
- Cited By
- 1
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412379019
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412379019Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s41205-025-00286-7Digital Object Identifier
- Title
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Machine learning-assisted finite element modeling of additively manufactured meta-materialsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-14Full publication date if available
- Authors
-
Alexander Meynen, H.M.A. Kolken, Michiel Mulier, Amir A. Zadpoor, Lennart ScheysList of authors in order
- Landing page
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https://doi.org/10.1186/s41205-025-00286-7Publisher landing page
- PDF URL
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https://threedmedprint.biomedcentral.com/counter/pdf/10.1186/s41205-025-00286-7Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://threedmedprint.biomedcentral.com/counter/pdf/10.1186/s41205-025-00286-7Direct OA link when available
- Concepts
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Finite element method, Metamodeling, Computer science, Meta learning (computer science), Materials science, Structural engineering, Engineering, Programming language, Systems engineering, Task (project management)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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33Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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