Titanium Alloy Repair with Wire-Feed Electron Beam Additive Manufacturing Technology
Bibliographic record
Abstract
Wire feeding can be combined with different heat sources, for example, arc, laser, and electron beam, to enable additive manufacturing and repair of metallic materials. In the case of titanium alloys, the vacuum operational environment of electron beam systems prevents atmospheric contamination during high-temperature processing and ensures high performance and reliability of additively manufactured or repaired components. In the present work, the feasibility of developing a repair process that emulates refurbishing an “extensively eroded” fan blade leading edge using wire-feed electron beam additive manufacturing technology was examined. The integrity of the Ti6Al4V wall structure deposited on a 3 mm thick Ti6Al4V substrate was verified using X-ray microcomputed tomography with a three-dimensional reconstruction. To understand the geometrical distortion in the substrate, three-dimensional displacement mapping with digital image correlation was undertaken after refurbishment and postdeposition stress relief heat treatment. Other characteristics of the repair were examined by assessing the macro- and microstructure, residual stresses, microhardness, tensile and fatigue properties, and static and dynamic failure mechanisms.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".