A 3D finite element model to simulate progressive damage in unidirectional- and woven-fibre thermoplastic discontinuous-long-fibre composites
Bibliographic record
Abstract
Discontinuous-long-fibre (DLF) composites fabricated from pre-impregnated unidirectional (UD) fibre chips are susceptible to structural deficiency. The in-plane highly anisotropic mechanical properties of the chips combined with the random nature of fibre orientation causes local weaknesses within the material when fibres are perpendicular to the load. Recent experimental results have shown that using woven-fibre chips could improve the performance of DLF composites by increasing their average mechanical properties and reducing their variability. To better understand the underlying phenomenon giving an advantage to the woven chips, a finite element model was developed to predict the mechanical properties obtained from a standard tensile test. DLF chips were modelled based on a voxel method where random chip positions were generated by an algorithm developed in this work. ANSYS® software was utilized to model the non-linear response associated with progressive damage of the composite. The maximum stress and the Puck failure criteria were employed to define damage initiation for the woven and UD fibres, respectively. Tensile modulus predictions for both types of chips showed good results when compared to experimental data. Strength predictions for the UD fibres also showed good correlation with experimental results, but the model overestimated the strength of the woven-fibre DLF composite. It appeared that the failure of the UD-fibre composites was associated with matrix failure (transverse tension and in-plane shear). Woven-fibre composites, however, showed damage modes linked to both fibre failure (longitudinal tension) and matrix failure (transverse tension and in-plane shear).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".