Constitutive Modeling of Damage Evolution in Semicrystalline Polyethylene
Bibliographic record
Abstract
In this article, the description of a novel damage-coupled constitutive formulation for the mechanical behavior of semicrystalline polyethylene is presented. The model attempts to describe the deformation and degradation processes in polyethylene considering the interplay between the amorphous and crystalline phases and following a continuum damage mechanics approach from a microstructural viewpoint. For the amorphous phase, the model is developed within a thermodynamic framework able to describe the features of the material behavior. Amorphous phase hardening is considered into the model and associated with the molecular configurations arising during the deformation process. The equation governing damage evolution is obtained by choosing a particular form based on internal energy and entropy. For the crystalline phase, the proposed model considers the deformation mechanisms by the theory of crystallographic slip and incorporates the effects of intracrystalline debonding and fragmentation. The model generated within this framework is used to simulate uniaxial tension and simple shear of high density polyethylene. The predicted stress-strain behavior and texture evolution are compared with experimental results and numerical simulations obtained from the literature. By incorporating a damage mechanics approach, the proposed model predicts the progressive loss of material stiffness attributed to the crystal fragmentation and molecular debonding of the crystal-amorphous interfaces.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".