Nanostructured Ni(Zn)–Al2O3 cermet particle synthesis in high-energy mechanical alloying method (CG-3:IL07)
Bibliographic record
Abstract
Ceramic-metallics (cermets) have emerged as an important class of composite material combining favorable properties of their constitutive phases. In this work, nanostructured cermet Ni(Zn)–Al 2 O 3 composition was synthesized using the high-energy mechanical alloying (HE-MA) method to be deployed as a cold spray deposition powder feedstock . Pre-milled Ni(Zn) alloy particles were mechanically mixed followed by milling in a SPEX™ 8000 M High Energy Ball Mill at a fixed 1050 rpm. The material design-of-experiment (DoE) involved two design compositions (30 wt% and 50 wt% α-alumina) with two substitutional alloy compositions, Ni(5 wt%Zn) and Ni(10 wt%Zn), along with milling time as a process variable. Microstructural characterization confirmed the embedment of nanoscale Al 2 O 3 grain in micron-scale Ni(Zn) alloy particles. Additionally, EBSD analysis of Ni(Zn) alloy particles revealed that Ni(5 wt%Zn) experienced more uniform plastic deformation , work hardening and subsequent fracturing when compared to Ni(10 wt% Zn) alloy particles. The particle size measurement was carried out by laser diffraction showed that the cermet particles milled for 4 h had the desired size range for the objective of cold spraying. Also, the embedment of alumina into the Ni(Zn) alloy matrix and thereby, homogenization of cermet particles enhanced with the increase in milling time.
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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".