Effect of Electrochemical Machining Time on Burr Removal Efficiency under Constant Electrolyte and Electrical Conditions
Bibliographic record
Abstract
Electrochemical machining has become the preferred precise deburring method for injector components with complex geometries that were machined by conventional techniques.However, the effect of machining time on deburring performance under fixed process conditions is still insufficiently explored in the literature.This study quantitatively evaluates the deburring efficiency of two exposure times-6 s and 12 s-while all electrical and electrolyte parameters are kept constant (80 A, 40 V, 125 mS cm⁻¹ NaNO₃, pH 7.6, 22 °C, 85 L h⁻¹).Ten specimens made of X4CrNiMo16-5-1 stainless steel (five per time level) were processed, and the outcomes were analysed by digital microscopy and high-precision weighing.Extending the machining time from 6 s to 12 s increased total mass removal (mean Δm: 0.0044 g → 0.0060 g) and produced a statistically significant reduction in burr area (55 % vs 146 %; p = 0.023).Conversely, the instantaneous material-removal rate decreased slightly (0.60 g s⁻¹ → 0.49 g s⁻¹).Short-duration runs exhibited high variability, including occasional burr growth-whereas longer exposures yielded more consistent and reliable results.These findings reveal a clear time-efficiency trade-off in electrochemical machining: moderate machining intervals strike an optimal balance between quality and productivity.The results provide a quantitative foundation for adaptive control strategies, cathode-design improvements, and simulation-based optimization in precision deburring applications.
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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".