Finite element prediction of curing micro-residual stress distribution in polymeric composites considering hybrid interphase region
Bibliographic record
Abstract
The interphase is a region between fibers and a matrix, which has different properties from the matrix and the fibers, but is dependent on them.Considering the interphase region has a significant effect on the accuracy of obtained residual stresses.So far, in order to obtain the micromechanical residual stresses, the interphase properties are considered as an average.In this paper, the properties of the interphase are assumed variable by using a suitable UMAT code in the ABAQUS software.The equations of previous studies that have acquired interphase properties to be variable are used to write the UMAT code.A representative volume element (RVE) in polymer composites is modeled in three phases in the ABAQUS software and the interphase properties are considered as FGM by using the UMAT code.Temperature variation during curing to environment temperature is the only loading factor in the RVE.The matrix, fiber and interphase stresses are obtained in the ABAQUS software.The achieved stresses were compared with the results of previous studies that considered interphase properties as average.Finite element and energy methods were used in previous papers but in the present study just the finite element method with variable interphase properties was use.In addition, residual stress diagrams with the variable interphase properties are plotted to study the effect of the thermal expansion coefficient.The results of this study are similar to those in previous ones, and the curves are improved.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".