Evaluation of Mechanical, Corrosion, and Wear Properties of Ti-6Al-4V and Ti-6Al-2Sn-4Zr-2Mo Welded by Nd:YAG Laser, pp. 173-180
Bibliographic record
Abstract
This study investigates the mechanical, tribological, and electrochemical corrosion properties of Ti-6Al-4V (Ti64) and Ti-6Al-2Sn-4Zr-2Mo (Ti6242) alloys welded using Nd:YAG laser welding, with a specific focus on aerospace applications.The research systematically evaluates tensile strength to assess weld integrity, pin-on-disc wear tests to analyze tribological performance, and electrochemical corrosion tests to determine resistance in aggressive environments.The influence of key laser welding parameters, including laser power, welding speed, and focal position, on weld bead geometry and performance was examined.The experimental results indicate that bead width varies between 3.46 mm and 3.92 mm, with laser power playing a significant role, while the depth of penetration remains consistent at approximately 2.21-2.23 mm.Wear analysis reveals that wear rate increases with applied load, sliding velocity, and.Electrochemical corrosion studies indicate significant variations in corrosion resistance among the welded samples, with the lowest corrosion rate (0.00036 mmpy) observed in EX2 and the highest (0.00354 mmpy) in EX4.The findings establish a correlation between tribological and electrochemical behavior, emphasizing the necessity of optimizing laser welding parameters to enhance durability and performance in aerospace applications.This study provides valuable insights into the interplay of wear and corrosion in laserwelded titanium alloys, contributing to the advancement of highperformance welding techniques.
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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".