Digital twin modelling for 3D-printed composite structures manufactured by fused filament fabrication method: mesoscale geometry simulation
Bibliographic record
Abstract
Fused Filament Fabrication (FFF) is a widely used additive manufacturing method for composite structures across various industries, including aerospace, energy, healthcare, and automotive. As the demand for advanced materials grows, Digital Twin (DT) modelling has emerged as a critical tool for bridging the gap between the real and virtual worlds of material design and testing. This study aims to develop a DT model for 3D-printed composite structures manufactured using the FFF method, leveraging Finite Element Analysis (FEA). The DT model is constructed using FEA, which simulates the mechanical behaviour of the composite structures. To ensure accuracy, the FEA results are validated by the Digital Volume Correlation method (DVC), a non-destructive, in-situ technique that measures the full strain field within the object. This enables real-time synchronization between the DT model and the physical state of the 3D-printed structures. The development of the DT model focuses on two key aspects: geometry definitions, and behavioral performance. The 3D geometry of the printed composite structures is captured using micro-computed Tomography (μCT). At the same time, initial mechanical properties are derived from in-situ experimental tensile testing conducted within the μCT system. The strain field measured by DVC is compared to the strain field predicted by the FEA model, and both the geometry and material properties are iteratively refined to minimize the error between the two. This methodology is applied to various printing parameters, such as raster orientation, to develop a robust dataset for training AI models to predict the mechanical behaviour of 3D-printed geometries. This study explains the definition of geometry for developing a DT model. In future work, the outcome will be compared with DVC analysis to create a highly accurate DT geometry model that improves the understanding and prediction of 3D-printed composite structures or any composite materials, providing valuable insights for optimization and design in industrial applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".