Facilitating the additive manufacture of high-performance polymers through polymer blending: A review
Bibliographic record
Abstract
Fused Filament Fabrication (FFF, a.k.a. fused deposition modeling, FDM) is presently the most widespread material extrusion (MEX) additive manufacturing technique owing to its flexibility and robustness. Nonetheless, it remains underutilized in load-bearing applications, as often seen in aerospace, automotive and biomedical industries. This is largely due to the processing challenges associated with high performance polymers (HPPs) like poly-ether-ether-ketone (PEEK) or polyetherimide (PEI). Compared with commercial-grade plastics such as polylactic acid (PLA), parts produced with HPPs have outstanding mechanical properties and thermal stability. However, HPPs have bulkier chemical structures and stronger intermolecular forces than common FFF feedstock materials, and this results in much higher printing temperatures and greater melt viscosities. The demanding processing requirements of HPPs have thus impaired their adoption within FFF. Polymer blending, which consists in properly mixing HPPs with other thermoplastics, makes it possible to alleviate these printing issues, while also providing additional advantages such as improved tensile strength and reduced friction. Further to this, manipulating the crystallisation processes of HPPs mitigates distortion or warping upon printing. This review explores some emerging trends in the field of HPP blends and how they address the challenges of excessive melt viscosity, polymer crystallization, moisture uptake, and part shrinkage in 3D printing. Also, the various structural/mechanical/chemical enhancements that are afforded to FFF parts through HPP blending are critically analysed based on recent examples from the literature. Such insights will not only aid researchers in this field, but also facilitate the development of novel, 3D printable HPP blends.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".