Reassessing anisotropy in 3D printed structures: The role of extrudate geometry vs interface bonding
Bibliographic record
Abstract
Mechanical performance of 3D-printed parts produced through fused filament fabrication (FFF) and fused granular fabrication (FGF) is governed by interface bond quality and extrudate geometry. This study investigates these factors in Polyethylene Terephthalate Glycol (PETG) parts printed at small (FFF) and large (FGF) scales, subjected to tensile testing along extrudate and transverse directions. Anisotropy in 3D-printed parts arises from incomplete interface bonding and formation of periodic ridges (interface notches) between extrudates. A machining method is used to remove the notches, enabling independent evaluation of these mechanisms. Results show that interface notches reduce part strength, with tensile strength decreasing by 28 % in small-scale and 70 % in large-scale samples. Digital image correlation (DIC) quantifies strain fields induced by the notches during loading, showing that the strain concentration factor (K ε ) decreases with smaller layer heights. Mesoscale finite element analysis (FEA), validated by DIC, confirms the experimental findings and highlights the critical influence of notch root radius in K ε . Once interface notches are eliminated, isotropic properties are achieved, demonstrating full bond strength. These findings emphasize the primary role of extrudate geometry in anisotropy and premature failure, guiding optimization of printing parameters and the design of next-generation 3D printing equipment aimed at mitigating these issues.
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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 | 0.000 |
| 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) | 0.001 | 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".