Effect of Ceramic Coated Tool on Stray Cut in Electrochemical Micromachining
Bibliographic record
Abstract
Electrochemical micromachining (ECMM) is an important machining process for machining materials susceptible to produce burrs during contact machining process.In this research copper plate of thickness 2mm and tool electrode of 464μm were machined through ECMM process under the sodium nitrate electrolyte (NaNO3).The tool electrode is coated with ceramic material and prevent stray current and chemical attack.The control factors namely, voltage, electrolyte concentration and duty cycle are varied on machining time and overcut.Higher voltage of 12V takes 1 minute to complete the through hole and the use of ceramic coated tool produces good arc on the circumference of the micro-hole and 65% reduction of overcut.The suitable range of electrolyte concentration for reduced machining time is 24 to 26g/lit.As per TOPSIS analysis, 10V, 24g/lit and 65% duty cycle and 11V, 24g/lit and 75% are the best optimal combinations.ANOVA results show that voltage and electrolyte concentration are the most significant factors with 57.73% and 28.83% contribution, respectively
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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 | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".