Study of Processing Conditions on the Forming of Ribbed Features Using Randomly-Oriented Strands Thermoplastic Composites
Bibliographic record
Abstract
Compression moulding of randomly-oriented strands composites offer the possibility to manufacture complex parts with fast processing cycle. In this paper, effects of pressure, strand size, temperature and material placement in the mould cavity on the quality of a T-shape part were studied experimentally. Low pressure results showed both strand size and temperature effects on the filling of a 25 mm rib deep cavity. Critical filling pressures for three strand sizes were obtained. A pressure of 10 bar was enough to fully consolidate parts with smaller strand (3.17 mm × 6.35 mm) at 400 °C. Parts processed at filling pressure showed a void content no greater than 1.2 %. Increasing pressure to 70 bar resulted in decreased void content between 0.44 % and 0.03 %. The lowest void content was obtained for parts processed at lower temperature. Short-beam shear (ASTM D2344) showed similar strength for ribs processed at filling pressure and high pressure (70 bar). This same trend was observed for component testing of the T-shape. At the component level, initial strand placement greatly affected mechanical performance as merging flow fronts caused a knit line and a reduction of at most 40 % in strength. The main findings show that processing a complex feature at filling pressure Pfill was sufficient to reach nominal mechanical properties. This suggested that moderate porosity was not detrimental to the mechanical performance for the given tests.
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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 | 0.000 | 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".