Multi-Material Direct Ink Writing (DIW) for Complex 3D Metallic Structures with Removable Supports
Bibliographic record
Abstract
Direct ink writing (DIW) combined with post-deposition thermal treatments is a safe, cheap, and accessible additive manufacturing (AM) method for the creation of metallic structures. Single-material DIW enables the creation of complex metallic 3D structures featuring overhangs, lengthy bridges, or enclosed hollows, but requires the printing supporting structures. However, the support printed from the same material becomes inseparable from the building structure after the thermal treatment. Here, a multi-material DIW method is developed to fabricate complex three-dimensional (3D) steel structures by creating a removable support printed from a lower melting temperature metal (i.e., copper) or a ceramic (i.e., alumina). The lower melting temperature metal completely infiltrates the porous steel structures for a hybrid configuration, while the ceramic offers a brittle support that can be easily removed. The influence of the support materials on the steel structure properties is investigated by characterizing the dimensional shrinkage, surface roughness, filament porosity, electrical conductivity, and tensile properties. The hybrid configuration (i.e., copper infiltrated steel structures) improves the electrical conductivity of the fabricated steel structure by 400% and the mechanical stiffness by 34%. The alumina support is physically and chemically stable during the thermal treatment, bringing no significant contamination to the steel structure.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".