Tough 3D printed biopolymeric cellular beams
Bibliographic record
Abstract
3D printing cellular structures with biobased polymers that possess high mechanical efficiency offer an attractive way for fabricating sustainable engineering materials. Herrin, two biobased polymers, polylactic acid (PLA) and polyamide-11 (PA-11) are melt-blended with and extruded as a co-continuous PLA/PA-11 filament for 3D printing. Fused deposition modelling (FDM) is used to manufacture samples which are then tested under quasi-static and impact loads, and the results demonstrate high impact toughness. Furthermore, a 200% increase in the impact toughness has been obtained by optimizing the print parameters. This indicates that optimization of 3D printing parameters is the key to additively manufacturing cost-efficient and eco-friendly parts that deliver mechanical/structural properties comparable to those made by injection molding. In addition, lightweight auxetic cellular beams are designed to reach a toughness-to-weight ratio much higher than the solid, hexagonal cellular, and rectangular cellular beam counterparts. An Ashby chart has been plotted, demonstrating impact toughness versus densities for comparing the performance of alternative polymers manufactured by FDM and injection molding. Finally, a 3D printed lightweight and tough biobased material is presented with significant potential for industrial applications.
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 | 0.000 |
| 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) | 0.002 | 0.001 |
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".