Sustainable metal-infused polymer feedstock compatible with low-cost metal sinter-based 3D printing
Bibliographic record
Abstract
Additive manufacturing has been employed to fabricate metallic parts; however, prevalent techniques are expensive and energy consuming. Therefore, fused deposition modeling (FDM) technique has grabbed the attention of researchers and industries. Despite the promising results, available materials for metal FDM 3D printing are very limited. The current study presents the development of a novel metal-infused polymeric feedstock for FDM 3D printing, consists of spiky-shaped recycled nickel powders and polylactic-acid (PLA) polymer matrix. A low-cost desktop 3D printer is employed to produce green parts; subsequently, debinding/sintering processes can be conducted to achieve a fully metallic part. The low-cost recycled nickel powder that has been used in this study is produced using the low-carbon footprint Mond process, with a significant application in production and recycling of nickel- and iron-based batteries. Furthermore, PLA is chosen because it is bio-based and biodegradable with a lower carbon footprint in the carbon cycle than fossil-fuel-derived polymers. Therefore, the whole process is an ecofriendly cycle, stepping toward the sustainable and affordable production of metallic components. Regarding development of a novel feedstock material compatible with 3D printing, it is important to understand its properties. So, the developed feedstock materials were rheologically and physico-mechanically analyzed to find the optimum filler concentration.
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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 | 0.001 |
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".