Thermal properties of glass fiber reinforced polyamide 6 composites throughout the direct long‐fiber reinforced thermoplastic process
Bibliographic record
Abstract
The direct long‐fiber reinforced thermoplastic (D‐LFT) process offers a streamlined material processing technique and decreases the degradation of the material. To ensure product consistency and process optimization, it is imperative to understand how the process sequence affects degradation and thermal properties of the material during the D‐LFT process. This study investigates variation in molecular weight and thermal properties of the glass fiber reinforced polyamide 6 (PA6) composites throughout the D‐LFT process. Viscosity number (VN) measurements, thermogravimetric analyses (TGA), and differential scanning calorimetry (DSC) analyses were performed on samples taken from different locations along the D‐LFT process. It was found that VN, which is a measure of molecular weight of the PA6 base resin, decreased throughout the processes. In contrast, TGA results suggested that apparent activation energy for decomposition increased during consecutive process stages. Non‐isothermal DSC results showed that there were no significant changes to the degree of crystallization; however, isothermal DSC results indicated that later stages of the process showed a decrease in crystallization half‐time, and the largest changes were observed in areas after the two extrusion portions of the process. POLYM. ENG. SCI., 58:46–54, 2018. © 2017 Society of Plastics Engineers
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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".