Impact of FDM Process Conditions on the Thermal and Mechanical Behavior of TPU 90A
Bibliographic record
Abstract
This study presents a comprehensive investigation of the thermomechanical and shape memory behavior of TPU 90A in both raw filament and 3D printed forms after manufacturing via Fused Deposition Modeling (FDM). The influence of key printing parameters extrusion temperature, infill density, and infill orientation on material performance is systematically examined. A novel UMAT subroutine was implemented in Abaqus to simulate the thermomechanical and shape memory responses of TPU 90A under varying loads and temperatures. Validation against experimental data confirms a pronounced shape memory effect, with shape fixity (Rf) and recovery (Rr) ratios exceeding 98%. The numerical model shows excellent agreement with experiments, as the simulated Young’s modulus (53.97 MPa) closely matches the measured value (55.38 MPa) for printed samples. Optimal mechanical properties were achieved at 230°C extrusion temperature, 45° infill orientation, and 100% infill density. The approach’s practical relevance is demonstrated through the fabrication of a personalized finger orthosis, where stress analysis indicates peak values of 17.76 MPa, ensuring sufficient rigidity for medical stabilization.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".