Improving the Impact Strength and Heat Resistance of 3D Printed Models: Structure, Property, and Processing Correlationships during Fused Deposition Modeling (FDM) of Poly(Lactic Acid)
Bibliographic record
Abstract
A fused deposition modeling method was used in this research to investigate the possibility of improving the mechanical properties of poly(lactic acid) by changing the thermal conditions of the printing process. Sample models were prepared while varying a wide range of printing parameters, including bed temperature, melt temperature, and raster angle. Certain samples were also thermally treated by annealing. The prepared materials were subjected to a detailed thermomechanical analysis (differential scanning calorimetry, dynamic mechanical analysis, heat deflection temperature (HDT)), which allowed the formulation of several conclusions. For all prepared samples, the key changes in mechanical properties are related to the content of the poly(lactic acid) crystalline phase, which led to superior properties in annealed samples. The results also indicate the highly beneficial effect of increased bed temperature, where the best results were obtained for the samples printed at 105 °C. Compared to the reference samples printed at a bed temperature of 60 °C, these samples showed the impact strength increased by 80% (from 35 to 63 J/m), HDT increased by 20 °C (from 55 to 75 °C), and also a significant increase in strength and modulus. Scanning electron microscopy observations confirmed the increased level of diffusion between the individual layers of the printed filament.
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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) | 0.000 | 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".