Water Droplet Erosion Performance of Laser Shock Peened Ti-6Al-4V
Bibliographic record
Abstract
The water droplet erosion (WDE) performance of laser shock peened (LSP) Ti-6Al-4V was investigated. LSP condition using two or three peening impacts per unit area induced compressive residual stresses. However, LSP treatment showed a mild increase in microhardness and no observable changes in the microstructure. The effect of LSP and its associated attributes on the WDE performance was studied according to the American Society for Testing and Materials Standard (ASTM G73). Influence of the impact speed between 150 and 350 m/s on the WDE performance was explored. Two sample geometries, T-shaped flat and airfoil, were used for the WDE tests. For the flat samples, LSP showed little or no beneficial effect in enhancing the WDE performances at all tested speeds. The peened and unpeened flat samples showed similar erosion initiation and maximum erosion rate (ERmax). The LSP airfoil samples showed mild improvement in the WDE performance at 300 m/s during the advanced erosion stage compared to the as-machined (As-M) condition. However, at 350 m/s, no improved WDE performance was observed for the LSP airfoil condition at all stages of the erosion. It was concluded that compressive residual stresses alone are not enough to mitigate WDE. Hence, the notion that the fatigue mechanism is dominating in WDE damage is unlikely.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".