Multiscale characterization and representation of composite materials during processing
Bibliographic record
Abstract
Given the importance of residual stresses and dimensional changes in composites manufacturing, process simulation has been the focus of many studies in recent years. Consequently, various constitutive models and simulation approaches have been developed and implemented for composites process simulation. In this paper, various constitutive models, ranging from elastic to nonlinear viscoelastic; and simulation approaches ranging from separated flow/solid phases to multiscale integrated phases are presented and their applicability for process simulation is discussed. Attention has been paid to practical aspects of the problem where the complexity of the model coupled with the complexity and size scaling of the structure increases the characterization and simulation costs. Two specific approaches and their application are presented in detail: the pseudo-viscoelastic cure hardening instantaneously linear elastic (CHILE) and linear viscoelastic (VE). It is shown that CHILE can predict the residual stress formation in simple cure cycles such as the one-hold cycle for HEXCEL AS4/8552 where the material does not devitrify during processing. It is also shown that using this simple approach, the cure cycle can be modified to lower the residual stress level and therefore increase the mechanical performance of the composite laminate. For a more complex cure cycle where the material is devitrified during a post-cure, it is shown that a more complex model such as VE is required. This article is part of the themed issue 'Multiscale modelling of the structural integrity of composite materials'.
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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) | 0.000 | 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 | 0.000 | 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".