Numerical implementation of a damage‐coupled material law for semicrystalline polyethylene
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide a computational procedure for a novel damage‐coupled material law for semicrystalline polyethylene. Using a damage mechanics approach, the model seeks to gain insight into the mechanical behaviour of polyethylene considering the microstructure and degradation processes occurring under uniaxial tension. Design/methodology/approach The material morphology is modelled as a collection of inclusions. Each inclusion consists of crystalline material lying in a thin lamella attached to an amorphous layer. The interface region interconnecting the two phases is the plane through which loads are carried and transferred by the tie molecules. It is assumed that the constitutive model contains complete information about the mechanical behaviour and degradation processes of each constituent. After modelling the two phases independently, the inclusion behaviour is found by applying some compatibility and equilibrium restrictions along the interface plane. Findings The model provides a rational representation of the damage process of the intermolecular bonds holding crystals and of the tie‐molecules connecting neighbouring crystallites. The model is also used to analyze the degree of relationship between some of the material properties and the mechanical responses. Practical implications In practice, the numerical model clearly helps to understand the influence of the different microstructure properties on the tensile mechanical behaviour of semicrystalline polyethylene – an issue of particular interest in improving material processability and product performance. Originality/value To the authors’ knowledge, a phenomenon such as microstructural degradation of polyethylene has not received much attention in the literature. The proposed model successfully captures aspects of the material behaviour considering crystal fragmentation and tie‐molecule rupture.
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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 | 0.001 | 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 | 0.000 | 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) | 0.004 | 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".