A Practical Investigation of the Production of Zr-Cu-Al-Ni Bulk Metallic Glasses by Arc Melting and Suction Casting
Bibliographic record
Abstract
The successful fabrication of bulk metallic glasses (BMG) through suction casting based on the existing literature is a difficult task due to the sensitivity of glass-forming ability (GFA) to small changes in processing variables. We report processing challenges and process modifications required in the successful and consistent production of Zr-Cu-Al-Ni BMGs by arc melting and suction casting. Focus was placed on homogenization methods, elemental yields, and the effect of argon purge gas and Zr purity on GFA. A “cut and re-cast” homogenization method used to reduce oxidation produced good overall homogeneity but resulted in the entrainment of an oxide-rich surface layer into the bulk of the alloy. Homogenization by multiple melting iterations and prolonged melting times was ultimately found to be the most effective method. Zr loss was observed in the bulk of the samples post-production. This has been attributed to the formation of a Zr/ZrO2 surface layer during melting. Using X-ray diffraction and isochronal DSC, both argon gas purity and Zr purity were shown to markedly affect GFA. GFA was optimized within a specific oxygen concentration range. The highest GFA was obtained when using high purity argon (Grade 6.0) and low Zr purity (99.5%). The optimization of GFA in Zr-based BMGs at a critical oxygen concentration has not been shown in previous work.
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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".