Visco-hyperelastic computational model for injection-molded thermoplastic polyurethane
Bibliographic record
Abstract
Accurate predictive modeling and simulation is crucial for designing and utilizing components in engineering, manufacturing, and other applications. To describe the complex mechanical response of injection-molded thermoplastic polyurethane (TPU), particularly its nonlinear elastic and viscous properties, we present a visco-hyperelastic computational model. The model is derived from a strain energy density function, formulated as a combination of elastic and viscous components. To account for compressibility of TPU, the elastic part of the strain energy function is further divided into isochoric and volumetric contributions. Mechanical characterization involved extracting standard specimens from an injection-molded slap of TPU and subjecting them to uniaxial loading at four distinct strain rates. The material parameters were identified through optimization using the Nelder-Mead algorithm. The presented model is then demonstrated to successfully capture TPU’s complex mechanical response with a unified set of parameters. Key advantages of the model include easy identification of material parameters, compliance with thermodynamic principles, ease of implementation in finite element analysis, and accurate description of large nonlinear elastic behavior and strain-rate dependency.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".