Effect Of Alloy Composition On The Atomization By EIGA Process
Bibliographic record
Abstract
Powder metallurgy is used in various industries such as aerospace, medical, defence, etc. For high cleanliness materials, metal powders can be produced using the EIGA process (Electrode Induction melting Gas Atomization). The EIGA process involves the crucible-free melting of an ingot followed by atomization using high-pressure argon. A full-scale EIGA is installed at MetaFensch|IRT M2P for R&D purposes (alloy development, numerical simulation, upscaling, etc.).Process parameters such as electrode size and gas parameters have a predominant influence on powder size and yields. On the other hand, chemical composition of the alloy and the associated thermophysical properties have a significant impact on the atomization process, and therefore on the quality of the powders. To better understand the effect of the composition, several alloys were selected (Ti64, titanium aluminide, nickel-based alloys, etc.) and atomized using the EIGA process. The properties of the powders, especially particle size distribution and morphology, are compared.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".