Optimizing the mechanical properties and print accuracy of 3D printed lightweighting continuous fibre reinforced polylactic acid foams
Bibliographic record
Abstract
Abstract Due to the urgent demand for lightweight and high-strength materials in rail transportation, this study proposed foamed polylactic acid (PLA) composites reinforced with continuous basalt fibres using a 3D printing technique to address the limitations posed by foaming-induced strength reduction in foam. Through a combination of parametric calculations, microscopic observations and compression experiments, the effects of printing parameters on the expansion ratio and print accuracy of foamed composite were investigated. It was found that adding fibres to foamed PLA reduced the expansion ratio of PLA by up to 9.52% at lower printing temperatures and layer heights but increased it at higher settings. The expansion ratio of the composite significantly increased with high printing temperatures and layer heights. When the composites were fabricated at low print temperatures and high layer heights, noticeable interlayer gaps and exposed fibres leading to poor impregnation were observed at cross-section. This phenomenon was improved as the expansion ratio increased. In addition, specimens with optimal print accuracy were prepared at specific combinations of printing temperature and layer height. In light of this discovery, a predictive function based on combined printing parameters was established to design composites with excellent print accuracy and specific densities. Finally, compression test results showed that with the same density of 0.5 g/cm3, the foamed composite exhibited substantial improvements in compressive strength, modulus and strain energy density compared to the foamed PLA, with increases of 44.44%, 57.02% and 24.19%, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".