High Frequency Vibration Fatigue Behavior of Ti6Al4V Fabricated by Wire‐Fed Electron Beam Additive Manufacturing Technology
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.
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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".