Enhancing Corrosion Resistance of Stainless Steel 304 Using Laser Surface Treatment
Bibliographic record
Abstract
Stainless steel AISI 304 was laser treated to enhance corrosion resistance and improve surface properties. . This alloy has many applications in auto industry (car body,) as well as oil and gas industry. Different conditions were applied in the laser surface treatment, namely: laser power density, scan speed, distance between paths, medium gas (air, argon and nitrogen). After laser treatment, the samples microstructures were investigated using optical microscope to examine micro structural changes due to laser irradiation. Specimen surfaces were investigated using XRD, SEM and EDAX before and after laser treatment to examine the surface composition changes brought by laser irradiation. Results showed that laser irradiation enhances the corrosion resistance of AISI 304 Stainless steel to a large extent. Corrosion rates as low as 0.011 mpy for laser treated samples were obtained in comparison to 0.952 mpy obtained for the untreated samples. Superior pitting corrosion resistance was obtained under specific treatment conditions. The enhancement of corrosion resistance depends on the laser irradiation conditions. The corrosion protection afforded by laser treatment is attributed mainly to the grain refinement of the top surface layer. This layer is found to consist of nano-scale grains.
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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 | 0.000 |
| 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".