Effects of process parameters on thermal properties of glass fiber reinforced polyamide 6 composites throughout the direct long‐fiber‐reinforced thermoplastics process
Bibliographic record
Abstract
The direct long‐fiber‐reinforced thermoplastic (D‐LFT) process is an efficient, one‐stop manufacturing process starting from raw materials to a final product, and includes various types of equipment. Tandem twin‐screw extruders are the main components of the D‐LFT process, and their control dictates productivity and properties of products. This study investigates the effects of extruder temperature and screw speed on molecular weight and thermal properties of glass fiber‐reinforced polyamide 6 (PA6) composites throughout the D‐LFT process. Viscosity number 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 viscosity number, which is a measure of molecular weight of the PA6 base resin, decreased with increasing extruder temperature and decreasing screw speed. In contrast, TGA results showed that the low screw speed of the extruders increased apparent activation energy of the final product. Non‐isothermal DSC crystallization analysis revealed no substantial changes to the material's degree of crystallinity with the variations in extruder temperature and screw speed; however, isothermal DSC crystallization analysis showed that the low screw speed of the extruders increased crystallization half‐time of the final material. POLYM. ENG. SCI., 58:E114–E123, 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".