Study the Effects of Dielectric Type on the Machining Characteristics of γ-Ti Al in Electrical Discharge Machining
Bibliographic record
Abstract
The current study surveys the results of using deionized water and kerosene as dielectrics in the machining outputs of γ-TiAl intermetallic compound obtained in electric discharge machining. Influences of these different dielectrics properties on machining speed, tool wear, surface cracks and roughness were compared. Scanning electron microscopy micrographs were prepared to investigate influences of dielectrics on the surface characteristics of electrically discharged samples. Results indicate which by kerosene dielectric; the material removal rate (MRR) is further compared to another one. But deionized water as dielectric causes higher tool wear ratio than kerosene dielectric. Electrical discharged samples in deionized water have higher surface roughness, in addition it contains surface cracks, whereas kerosene dielectric results better surface finish in low pulse current. According to XRD spectra electric discharge machining in deionized water and kerosene dielectrics produces Ti3 Al intermetallic compound on the produced surface.
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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 | 0.001 |
| 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".