Computational modeling of constitutive behaviour of 3D printed composite structures
Bibliographic record
Abstract
Anisotropy is one of the main limitations in the effective design of 3D printed structures. Further, experimental evaluation of the material behaviour of printed parts is tedious, and expensive. An alternate solution is computational material modeling of printed parts. In the present work, the computational homogenization is applied to estimate the final constitutive behaviour of parts printed with polymeric composite material. To begin, the mesostructure of layers of the printed parts was considered for finite element modeling of the representative volume element (RVE), and to determine their elastic moduli. Computationally estimated material properties were higher than experimental values, and the percent difference between them was higher for parts made of ABS polymer containing short carbon fibers (sCF) versus ABS alone. The computational models provided more insights on the final properties of 3D printed parts for different materials. Further, the stress contours of the RVEs revealed that the printed parts are prone to two different failure types: delamination and fiber pull-out. Also, the lateral and transverse elastic moduli of layers were found to be approximately the same, and therefore the constitutive behaviour of the layers can be treated as transversely isotropic material. In summary, this research work represents an important step towards enabling the effective design and analysis of 3D printed structures using computational methodology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".