Process-induced skewness of flow fronts and fiber orientations in LFT-D compression molding considering processing, characterization, and simulation
Bibliographic record
Abstract
Mechanical properties of long fiber-reinforced thermoplastic (LFT) materials are defined by the fiber microstructure, including fiber orientation (FO). Compression molding of directly compounded LFT-D semi-finished materials results in pronounced anisotropy caused by fiber orientation mechanisms during material flow, with fibers aligning in flow direction. However, various authors noticed a deviation of the FO from the anticipated flow direction, which affected the mechanical properties and possibly blurred experimental conclusions. This study investigates possible reasons for this phenomenon, considering the process chain from plastificate and molding to final FO. For this purpose, we conduct a comprehensive mold-filling study comprising short shots and plates for mechanical characterization. A method is presented to characterize the skewness of the flow front of short shots. In addition, a method for deriving FO from tensile discs is applied. Both results are compared to a state-of-the-art simulation in which results from the characterization of the semi-finished LFT material, the plastificate, are considered. The results indicate that the density distribution of the LFT-D plastificate is not homogeneous; the influence of extrusion time causes a density gradient of around 10 %. This can be traced across the mold filling, where the flow fronts as well as the FOs are skewed towards the most recently extruded portion of the LFT, as well as to the FO. An FO deviation from the flow direction between 10° and 15° is found.
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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".