Effect of raster angles on the anisotropic behavior of 3D-printed TPU under uniaxial and planar loading conditions
Bibliographic record
Abstract
Thermoplastic polyurethane (TPU) is typically modeled as an isotropic hyperelastic material. However, when fabricated using fused deposition modeling (FDM), it exhibits anisotropic behavior due to directional filament alignment. This study investigates the effects of raster orientation and layer height on the mechanical performance of 3D-printed TPU using uniaxial and planar tensile tests. Four raster angles (0°, 90°, 0/90°, and -45/45°) were tested under uniaxial loading, while planar tests used 0° and 90° orientations. The 0° raster, aligned with the loading direction, showed the highest tensile strength (~9.5 MPa), modulus (~22.85 MPa), and elongation (>250%). The 90° raster displayed the lowest performance, with early failure (~90-100% strain), lower strength (~4-8 MPa), and reduced stiffness (~19.34 MPa), due to weak interlayer bonding. Bidirectional raster angle showed intermediate responses. Planar tensile results confirmed this anisotropy, with 0° specimens reaching ~20 MPa and 230% strain, while 90° specimens failed at ~5 MPa and ~50-60% strain. Microscopy images revealed that reducing layer height from 0.24 mm to 0.18 mm improved interlayer bonding but narrowed raster-to-raster bond widths. These results highlight the strong influence of printing parameters on TPU anisotropy and the need for constitutive models that account for directional effects in 3D-printed soft materials.
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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".