Investigating the nanoscale hardness/strength properties of high-entropy alloy particles using the nanoindentation technique
Bibliographic record
Abstract
Particulate feedstock constitutes the building block in modern day additive manufacturing (AM) era. Cold spray (CS) is a leading process technology to adhere to the AM principle. Therefore, meeting feedstock qualities is of utmost interest to ensure the conformability and competitiveness of the developed industrial modules, including coatings, architectured components, and additively repaired devices. This research advances the understanding of nanoscale hardness/strength properties of particulate matters, specifically of an emerging material class, - high-entropy alloys (HEAs). The feasibility of determining the hardness of mechanically alloyed AlCoCrFeNix (x = 0, 1, 2.1) HEA particles was studied employing the nanoindentation technique. Mechanical properties of milled AlCoCrFeNix particles with varying Ni atomic ratio (x = 0, 1, 2.1) were investigated over different milling times ranging between 4 to 24 hours. The study analyzed the impact of mounting resin, pre-determined maximum load, and indentation depth on hardness/strength properties. Results reveal that the hot mounted samples yielded greater accuracy and higher hardness values than compared to those of the cold mounted samples. Additionally, although the low-load sensitivity of AlCoCrFeNix provided consistent nano-scale hardness values across selected loads, their hardness values were found to be depth-dependent. Overall, the study concludes with a methodology for the nano-scale hardness/strength measurement of HEA particles that must account for particle size, sample preparation technique, and nanoindentation test parameters.
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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".