Polymer blends as a tool to improve mechanical properties and printability of metal-filled polymer filaments for material extrusion additive manufacturing in the context of sustainable manufacturing
Bibliographic record
Abstract
This study reports the development and optimization of highly metal-loaded thermoplastic composite filaments for material extrusion additive manufacturing of metallic components. Nickel (Ni) and iron (Fe) powders with varying particle sizes and morphologies were combined with polyethylene (PE), polylactic acid (PLA), and PE/PLA blends as binders. The influence of particle characteristics and binder composition on filament morphology, mechanical properties, porosity, thermal behavior, and printability was systematically investigated. Composite filaments containing up to 90 wt% Ni and 80 wt% Fe were successfully extruded. Scanning electron microscopy revealed that fine Ni particles improved dispersion and reduced porosity, whereas coarse Fe particles resulted in heterogeneous packing. Thermal analyses guided debinding and sintering conditions, while mechanical testing demonstrated that PE enhanced flexibility, PLA contributed to strength, and blended systems offered a balanced compromise with good printability. Optimized 3D printing parameters enabled the fabrication of high-quality green parts, which were successfully debound and sintered using graphite powder to suppress oxidation. Dense metallic structures with controlled shrinkage and minimal residual porosity were obtained. Ni-based samples exhibited greater shrinkage and cracking due to finer particle size and higher thermal expansion. The results demonstrate a robust materials–process design strategy for FFF of metals. Unlike conventional multi-step solvent-based methods, this work employs a simple dry-mixing route and standard laboratory furnace processing without vacuum or inert atmospheres. This streamlined approach provides an environmentally friendly and scalable pathway for additive manufacturing of high-performance metallic parts.
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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".