Experimental study and numerical simulation of defect formation during compression moulding of discontinuous long fibre carbon/PEEK composites
Bibliographic record
Abstract
Composite materials continue to replace metal in a growing number of applications due to their recognized performance, tailorability, life-cycle, and manufacturing advantages.While continuous fibre composites are the primary materials employed to replace metallic components in aerospace applications, their current use is generally limited to large shell-like structures.There is thus an emerging interest in the aerospace industry to use composite materials at a smaller scale to replace complex-shaped metallic components.This presents some unique challenges, mainly because traditional continuous fibre composite materials are practically unusable for this type of application, while short-fibre injection moulded parts have limited mechanical properties, although being highly versatile geometrically.Lying between these two extremes are discontinuous long fibre (DLF) composites, a bulk moulding compound type of material that can be compression moulded into complex-shaped parts.This technique has been shown to be very effective for moulding net-shaped components having features such as varying wall thickness, tight radii, reinforcing ribs, flanges, mould-in holes, etc.However, the increase in part complexity introduces manufacturing problems.One problem in particular arises during processing of thermoplastic composites, where inconsistent part quality may occur if the consolidation pressure is lost before solidification of the matrix during cooling.Such a phenomenon can be difficult to predict due to the complex nature of DLF composite parts.Given that understanding and predicting defect formation is crucial to achieving success in manufacturing of complex-shaped composite parts, a threefold approach was used in this thesis to study the phenomena that influence this behaviour.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".