Polymorphism and Mechanical Behavior in Hot-Pressed 3D-Printed Polyamide Composite: Effects of Pressure and Temperature
Bibliographic record
Abstract
The aim of this work is to study the effect of high-temperature compaction (HTC) upon the polymorphism and the mechanical behavior of an additively manufactured (AM) carbon fiber-reinforced polyamide (PA6). Different pressure and temperature levels during HTC were tested to determine the overall effect on the mechanical behavior and material crystalline composition. Treated, carbon fiber-reinforced PA6 samples were analyzed using differential scanning calorimetry, X-ray diffraction, thermogravimetric analysis, scanning electron microscopy, and three-point bending testing. When considered with respect to as-printed samples, an HTC temperature of 190 °C combined with 80 psi pressure resulted in an increased flexural modulus and strength of 47% and 58%, respectively. This increase was attributed to the decrease in AM-induced cracking, voids (both inside and between the beads), and crystalline solid-state transition in the PA6. The effect of pressure and temperature on the crystalline structure was discussed in terms of an increased degree of crystallinity and the amount of α-phase. Therefore, HTC can help overcome some limitations of traditional annealing, which can result in recrystallization-induced cracking which can lead to material embrittlement. The proposed HTC method demonstrates the potential in improving the mechanical behavior of AM thermoplastic composites.
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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".