Modelling and optimizing surface roughness and MRR in electropolishing of AISI 4340 low alloy steel in eco-friendly NaCl based electrolyte using RSM
Bibliographic record
Abstract
Abstract Electropolishing (EP) is a reliable post-processing method of the drilled metals for achieving a high-quality surface finish with an appropriate material removal rate. This process has many applications due to its advantages such as improving the surface quality by removing the surface peaks on a micro-scale. The aim of most attempts on this process is setting up the optimum parameters to obtain maximum Material Removal Rate (MRR) with minimum surface roughness. In the present wo k, electropolishing of AISI 4340 low alloy steel immersed in eco-friendly NaCl solution has been studied numerically and experimentally. So, primarily a simulation model was developed for the EP process on cylinder parts in COMSOL Multiphysics which was validated with experimental approaches. The results revealed that the numerical model would be convenient for EP. The experiments were performed using Response Surface Methodology (RSM) to evaluate the effect of input variables on the responses. The effects of input variables electrolyte temperature, current intensity, and primary gap were investigated on the outputs MRR and surface roughness at five levels. Based on the results, the electrolyte temperature and current intensity were more effective parameters on the outputs. Results of ANOVA and regression analysis approach revealed that by increasing the current and electrolyte temperature, the MRR increases correspondingly and surface roughness declines and the primary gap has a reverse effect on the MRR. Finally, by performing a multi-objective optimization using Derringer’s desirability approach, the EP of AISI 4340 with an eco-friendly NaCl solution was optimized.
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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 | 0.001 |
| 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 | 0.001 | 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".