Computational study on a damage‐coupled model for crystalline polyethylene
Bibliographic record
Abstract
Purpose The purpose of this paper is to formulate an algorithm for a novel damage‐coupled material law for crystalline polyethylene at finite inelastic strains followed by investigation of the influence of the aggregate representation and material parameters on the material response. Design/methodology/approach The constitutive equations are developed within the framework of continuum damage mechanics to describe crystal fragmentation caused by atomic debonding of the crystallographic planes. The material is assumed initially isotropic and homogeneous and is represented as an aggregate of randomly oriented crystals with an orthorhombic lattice. For the velocity gradient, an additive decomposition into symmetric and skew‐symmetric components is applied, where the skew‐symmetric part (spin) is decoupled from the lattice shear by means of a damage variable. Structural features such as lattice parameters and orientations, slip systems, and kinematic constraints are incorpo‐rated. Findings The proposed model is implemented to predict stress‐strain behaviour under uniaxial tension and damage accumulation and texture development at the different stages of deformation. In the numerical examples, the effects of the aggregate size, crystal orientations, and material parameters on the model estimates are analyzed. Originality/value The model used herein is a first attempt to analyze the influence of crystal fragmentation caused by the debonding of the crystallographic planes on the predicted mechanical behaviour and texture development of polyethylene prior to failure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".