High Frequency Vibration Fatigue Behavior of Ti6Al4V Fabricated by Wire‐Fed Electron Beam Additive Manufacturing Technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Following foreign object damage (FOD), a decision to repair components using novel additive manufacturing (AM) technologies has good potential to enable cost‐effective and efficient solutions for aircraft gas turbine engine maintenance. To implement any new technology in the gas turbine remanufacturing world, the performance of the repair must be developed and understood through careful consideration of the impact of service life‐limiting factors on the structural integrity of the component. In modern gas turbine engines, high cycle fatigue (HCF) is one of the greatest causes of component failure. However, conventional uniaxial fatigue data is inadequate in representing the predominant HCF failure mode of gas turbine components that is caused by vibration. In this study, the vibratory fatigue behavior of Ti6Al4V deposited using wire‐fed electron beam additive manufacturing (EBAM) was examined with the motivation of developing an advanced repair solution for fatigue critical cold‐section parts, such as blades and vanes, in gas turbine engine applications. High cycle fatigue data, generated using a combination of step‐testing procedure and vibration (resonance) fatigue testing, was analyzed through Dixon–Mood statistics to calculate the endurance limits and standard deviations of the EBAM and wrought Ti6Al4V materials. Also plots of stress (S) against the number of cycles to failure (N) were obtained for both materials. The average fatigue endurance limit of the EBAM Ti6Al4V was determined to be greater than the wrought counterpart. But the lower limit (95% reliability) of 426 MPa for the EBAM Ti6Al4V was lower than the value of 497 MPa determined for wrought Ti6Al4V and was attributed to the slightly higher data scatter–as reflected by the higher standard deviation–of the former material.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it