Electrochemical dissolution behavior of stainless steels with different metallographic phases and its effects on micro electrochemical machining performance
Bibliographic record
Abstract
To investigate the anodic dissolution of stainless steels with diverse metallographic phases and its impact on micro electrochemical machining (micro-ECM) performance, the electrochemical behavior of representative ferritic stainless steel (SUS430), martensite stainless steel (SUS440C), dual-phase stainless steel (2205 DSS), and austenite stainless steel (SUS316L) in neutral solutions were examined by analyzing potentiodynamic results and electrochemical impedance spectroscopy (EIS). The growth and chemical compositions of passive films on the stainless steels were evaluated using X-ray photoelectron spectroscopy (XPS) and Auger electron spectroscopy (AES). EIS results indicate that the passive film formed on SUS430 (ferritic phase) and SUS440C (martensite phase) in NaClO3 exhibited greater stability compared to that formed in NaNO3, which is attributed to the thicker passive film formed in NaClO3 and higher Fe2+/Fe3+ ratio as well as Cr2O3/Cr(OH)3 ratio. The SUS316L (austenite phase) exhibits almost the opposite behavior compared to the SUS430 and SUS 440C. The impact of the electrochemical behavior on the evolution of dissolution region and surface topography was discussed from the micro-ECM experimental results. The 2205 DSS exhibits a much higher corrosion resistance, but local corrosion zones were observed at the edges of microgrooves, leading to approximate material removal rate values compared to single-phase steels. The metallographic phases influence the composition, structure, and density of the passive films, and determine the ECM performance. This study demonstrates the relationships between the MRRs and the metallographic phases of stainless steel, which provides a feasible idea for optimizing the matches of the electrolyte composition and stainless steel workpiece.
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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) | 0.000 | 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".