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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it