Enhancing corrosive-wear resistance of 316 austenitic stainless steel via surface texturing and double glow plasma surface alloying with titanium (Ti)
Bibliographic record
Abstract
316 austenitic stainless steel exhibits significant corrosion failure in high-salt and friction-coupling environments, leading to substantially shortened service life in marine engineering and medical device applications. This highlights the necessity of surface treatment technologies to enhance material performance. To improve corrosive-wear resistance of 316 austenitic stainless steel in simulated seawater, surface texturing combined with double glow plasma surface alloying with titanium (DGPST) was conducted. Four samples were received: untreated substrate (G-316SS), DGPST-treated (T-316SS), surface textured (ST-316SS), surface textured and DGPST (DT-316SS). Their microstructure, mechanical properties, wear, and corrosion resistance were analyzed using XRD, SEM, nanoindentation, electrochemical tests, and friction/wear tests in simulated seawater. Results showed the composite DT-316SS sample achieved the highest corrosion potential, about −0.42 V, indicating superior corrosion resistance. The texture reduces friction contact area and stores lubricant, enhancing tribological performance. The T-316SS has better overall tribological properties. This is attributed to its dense layer rich in titanium, which enhances its tribological properties. The wear test results showed that the composite-treated and DGPST-treated samples had lower friction coefficient and wear rate. The surface hardness of the treated titanium is twice as high as that of the stainless steel matrix, up to 6.5 GPa, and the wear rate is reduced from 1.2 × 10 - 5 mm 3 /(N·m) to 0.32 × 10 - 5 mm 3 /(N·m).
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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".