Influence of Carbon Fiber Reinforcement on Mechanical and Thermal Behavior of PLA and PAHT in Additive Manufacturing
Bibliographic record
Abstract
This study explores the comparative evaluation of PLA, carbon fiber-reinforced PLA (PLA-CF), and carbon fiber-reinforced high-temperature polyamide (PAHT-CF) for use in Fused Deposition Modeling (FDM) additive manufacturing. These materials were selected to examine how carbon fiber (CF) reinforcement affects PLA and PAHT, using virgin PLA as the baseline. Mechanical and thermal properties were tested to assess the influence of reinforcement on strength, toughness, and heat transfer. Tensile, impact, and thermal conductivity tests were conducted on all three materials. The results showed that PAHT-CF outperformed both PLA and PLA-CF in all categories, achieving an ultimate tensile strength of 57.5 MPa, an impact strength of 14.30 kJ/m2, and thermal conductivity of 0.182 W/m·K. PLA-CF showed moderate improvements in strength over neat PLA but with increased brittleness and slight improvement in thermal conductivity. Notably, this is the first study to investigate the thermal conductivity and resistivity of PAHT-CF in the literature, offering new insights into its heat dissipation capabilities and suitability for high-temperature applications. These findings highlight the critical role of polymer selection and fiber reinforcement in optimizing material performance. The results offer guidance for material selection in additive manufacturing, especially for lightweight, strong, and thermally efficient parts in various industries.
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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".