Tailoring absorptivity of highly reflective Ag powders by pulsed-direct current magnetron sputtering for additive manufacturing processes
Bibliographic record
Abstract
Processing of highly reflective and high thermally conductive materials (Cu, Ag, etc.) by laser powder bed fusion (LPBF) is of increasing interest to broaden the range of materials that can be additively manufactured. However, these alloys are challenged by high reflectivity resulting in unmelted particles and porosity. This is exacerbated for in-situ alloying techniques, where divergent optical properties of blended powders further narrow the stable processing window. One possible route to improved uniformity of initial melting is through coating powders with an optically absorptive layer. In-situ alloying of Ti-Ag was chosen as a model to assess this, given the potential of Ti-Ag as a novel antimicrobial biomedical alloy, facilitating an ideal model to assess this approach. High purity Ag powder was coated with Ti via physical vapour deposition. Barriers to reliable coating were investigated, with agglomeration of particles observed at a sputtering power of 100 W. In-situ laser micro calorimetry demonstrated a significant improvement in melting performance for coated Ag powder, with continuous tracks attained at 280 W vs. 320 W for uncoated powder, and absorptivity increasing from 27 % to 45 % at 320 W incident laser power. Subsequent in-situ alloying of the Ag powder when blended with commercially pure Ti powder demonstrated that improved absorptivity allowed for more uniform densification of the blended powder bed at lower energy density (0.7 ± 1.0 vs 7.1 ± 2.0 % porosity at 133 J.m-1). Ultimately, this offers a promising route to improved alloy development via LPBF, through application of a homogeneous, relevant coating.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".