4D printing and programming of continuous fibre-reinforced shape memory polymer composites
Bibliographic record
Abstract
This study demonstrates the use of fused filament fabrication (FFF) 4D printing (4DP) to print programmable continuous fibre-reinforced composite (CFRC) structures with exceptional strength and eco-friendly features. This research focuses on bio-shape memory polymer composites (SMPCs) and employs experiments to fabricate lightweight CFRC parts using FFF technology. Different types of continuous fibres, including carbon fibre (CF), aramid fibre (AF), and fibreglass (FG), are incorporated into a biopolymer matrix made of biodegradable polylactic acid (PLA). The study evaluates microstructure, mechanical properties, and shape memory properties of SMPCs, employing techniques like cold and hot programming. Continuous fibres significantly enhance mechanical properties, increasing strength by over 1027.5 % in tensile tests and nearly 497.3 % in three-point bending tests. The research also addresses shape recovery and fixity ratios in 4D-printed SMPCs, finding a decrease when continuous fibres are incorporated into PLA. Notably, FGPLA specimens achieve the highest shape recovery ratio of approximately 95 ± 1 % after pure PLA. These findings highlight the potential of 4D-printed CFRCs in various applications, from human-material interaction to mechanical and biomedical fields. They contribute to sustainability by reducing material consumption and waste, demonstrated through the creation of reusable and lightweight items like hooks, lockers, finger splints, and meta-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".