Influence of 3D printing process parameters on the anisotropic viscoelastic behavior of thermoplastic polyurethane (TPU)
Bibliographic record
Abstract
Additive manufacturing, particularly 3D printing, has revolutionized the creation of complex geometries and expanded the applications of materials, including elastomers. Despite these advancements, understanding how 3D printing process parameters affect the anisotropic viscoelastic behavior of 3D-printed elastomers remains a critical challenge. This study investigates the mechanical behavior and stress relaxation characteristics of 3D-printed thermoplastic polyurethane (TPU) under varying drying conditions, printing temperatures, raster angles, and strain rates. Tensile and relaxation tests reveal a strong strain rate dependency and the critical influence of filament drying on mechanical properties. Specimens dried for 8 hours exhibit higher stress and modulus of elasticity than those dried for 3 hours, highlighting the role of moisture removal in enhancing stiffness. Comparisons between 230°C (8-hour drying) and 250°C (3-hour drying) specimens confirm that extended drying improves mechanical performance more than a change in printing temperature. The anisotropic nature of 3D-printed TPU is evident in raster orientation effects, where 0° raster specimens exhibit superior strength, modulus of elasticity, and large deformation, while 90° raster specimens fail at lower strain values due to weaker interlayer adhesion. Stress relaxation tests further confirm that 0° raster specimens experience higher total and equilibrium stresses, leading to more significant absolute stress decay. These findings provide key insights into optimizing 3D printing parameters to improve the mechanical reliability and long-term performance of TPU components in engineering applications.
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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".