Investigation of mechanical and wettability properties of commercial pure titanium by surface mechanical attrition treatment
Bibliographic record
Abstract
The examination of mechanical and wettability properties of commercial pure titanium (CP-Ti) introduced a novel challenge. The microstructure of samples was analyzed by using OM and SEM micro-images. In this study, the hardness of the surface and cross section of samples was evaluated, and the effect of several surface mechanical attrition treatments (SMATed) on samples was examined. Also, the tensile test was done to compare the effect of SMAT duration on mechanical properties of samples. The contact angle method was then employed to investigate the impact of SAMT duration on the wettability trend. According to results, as the treatment time increases, twins become evident in the deeper layers of the samples. In contrast, the near-surface twins are shorter than those found in the deeper material, signifying the presence of finer grains. Moreover, the elevated hardness in SMATed samples can be attributed to several key factors, including cold work, a rise in defects and twins, and grain refinement. The surface hardness increases across the sample series (up to Ti6) due to the substantial energy input. Nevertheless, it is anticipated that further continuation of the process will result in a constant surface hardness. The increased strength observed in SMATed samples can be attributed to factors such as reduction in grain size and the formation of twins. The wettability results show that the treated surfaces exhibited a significant change in wettability compared to the untreated samples, with a 25% reduction in the contact angle. This is attributed to the surface modifications induced by the treatment process, which introduced more hydrophilic functional groups. The results also show that after 4 h of treatment, the contact angle increases, suggesting that the trend may plateau with additional treatment time.
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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 | 0.001 | 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".